system

Abstract

The system according to the embodiment aims to provide support for children to develop the ability to think for themselves. [Solution] A system according to an embodiment includes a reception unit, a provision unit, a guide unit, and a feedback unit. The reception unit receives a question. The provision unit provides a hint based on the question received by the reception unit. The guide unit guides a user on how to research based on the hint provided by the provision unit. The feedback unit provides feedback on the results of the research based on the research method guided by the guide unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of lacking adequate support to help children develop the ability to think for themselves.

[0005] The system according to the embodiment aims to provide support for children to develop the ability to think for themselves. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a provision unit, a guide unit, and a feedback unit. The reception unit receives a question. The provision unit provides a hint based on the question received by the reception unit. The guide unit guides a user on how to research based on the hint provided by the provision unit. The feedback unit provides feedback on the results of the research based on the research method guided by the guide unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide support for children to develop the ability to think for themselves. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The educational support system according to an embodiment of the present invention is a children's version of a generative AI system designed to develop children's ability to think independently. This system aims to help children develop the ability to solve problems independently by clearly explaining ways of thinking and researching, rather than directly teaching them the answers. For example, it includes a question acceptance function, a hint provision function, a research guidance function, and a feedback function. With the question acceptance function, a child inputs a question, and the AI ​​accepts the question. For example, suppose the question is, "How do plants grow?" Next, with the hint provision function, the AI ​​does not provide a direct answer to the question, but instead provides hints on how to think and research. For example, it might provide a hint such as, "Think about what plants need to grow. Water, light, soil, etc. are involved." With the research guidance function, the AI ​​guides children on how to research independently. For example, it might teach them specific research methods, such as, "Look for books about plants at the library. You could also try searching for 'plant growth conditions' on the Internet." With the feedback function, a child reports the results of their own research to the AI, and the AI ​​provides feedback on the content. For example, it provides feedback such as, "You've done a good job of researching. You've learned that water and light are important for plant growth. Next, let's find out how much water is needed." In this way, the children's version of generative AI promotes independent learning by supporting children's processes of thinking, researching, and learning on their own. In addition, the AI ​​adjusts the content of hints and research guides according to each child's level of understanding, allowing it to provide support that matches each child's learning pace. In this way, the educational support system supports children's processes of thinking, researching, and learning on their own, promoting independent learning.

[0029] An education support system according to an embodiment includes a reception unit, a provision unit, a guide unit, and a feedback unit. The reception unit receives a question input by a child. For example, if a child inputs a question such as, "How do plants grow?", the reception unit receives the question. The provision unit does not provide a direct answer to the question, but provides hints on how to think about and research the question. For example, the provision unit may provide a hint such as, "Think about what plants need to grow. Water, light, soil, etc. are involved." The guide unit guides the child in how to research the question on their own. For example, the guide unit may provide specific research instructions such as, "Look for a book about plants at the library. You could also try searching for 'plant growth conditions' on the Internet." When the child reports the results of their research to the AI, the feedback unit provides feedback on the content. For example, the feedback unit may provide feedback such as, "You did a good job of researching. You now know that water and light are important for plant growth. Next, let's find out how much water and light are needed." This allows the education support system according to an embodiment to support children's processes of thinking, researching, and learning on their own, thereby promoting independent learning.

[0030] The providing unit may provide hints on how to think about or research questions, rather than providing direct answers to questions. For example, the providing unit may provide hints on how to think about or research questions, rather than providing direct answers to questions. For example, the providing unit may give a hint such as, "Think about what plants need to grow. Water, light, soil, etc. are involved." The providing unit may also analyze questions and generate appropriate hints. For example, the providing unit may analyze the content of the question and generate hints based on related information. This allows the providing unit to develop children's ability to solve problems on their own. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input a question into a generation AI, which then generates a hint.

[0031] The guide unit can guide children on how to do their own research. For example, the guide unit can guide children on how to do their own research. For example, the guide unit can teach specific research methods, such as, "Look for a book about plants at the library. You could also try searching for 'plant growth conditions' on the Internet." The guide unit can also guide children on how to find books at the library and how to search on the Internet. For example, the guide unit can teach children how to use classification numbers and how to read the table of contents to find books at the library. The guide unit can also teach children how to use search engines and how to select keywords to search on the Internet. In this way, the guide unit can cultivate children's ability to do their own research. Some or all of the above-mentioned processing in the guide unit may be performed using, or without, a generation AI. For example, the guide unit can input a research guide into the generation AI, which then generates the guide.

[0032] The feedback unit can provide feedback to the child based on the results of their own research. For example, when a child reports the results of their own research to the AI, the feedback unit provides feedback on the content. For example, the feedback unit may provide feedback such as, "You did a good job of researching. You learned that water and light are important for plant growth. Next, let's find out how much water is needed." The feedback unit can also estimate the child's emotions and adjust the content of the feedback based on the estimated child's emotions. For example, the feedback unit can provide simple and easy-to-understand feedback if the child is excited. The feedback unit can also provide feedback with detailed explanations if the child is relaxed. The feedback unit can also provide short and to-the-point feedback if the child is tired. This allows the feedback unit to confirm what the child has learned and obtain feedback to move on to the next step. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit can input the results of the research into the generation AI, which then generates feedback.

[0033] The providing unit can analyze the question and generate an appropriate hint. The providing unit, for example, analyzes the question and generates an appropriate hint. For example, the providing unit analyzes the content of the question and generates a hint based on related information. The providing unit can also use natural language processing technology or keyword extraction technology as a question analysis method. For example, the providing unit can analyze the meaning of the question using natural language processing technology and extract related information. The providing unit can also extract important keywords in the question using keyword extraction technology and generate a hint based on the keywords. In this way, the providing unit can deepen children's understanding by providing appropriate hints for the question. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input a question to a generation AI, which can generate a hint.

[0034] The guide unit can provide guidance on how to find books in a library and how to search the Internet. The guide unit provides guidance on, for example, how to find books in a library and how to search the Internet. For example, the guide unit teaches how to use classification numbers and how to read the table of contents as ways to find books in a library. The guide unit also teaches how to use a search engine and how to select keywords as ways to search the Internet. In this way, the guide unit allows children to learn specific research methods. Some or all of the above-mentioned processing in the guide unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the guide unit can input a guide on how to search into a generation AI, which then generates a guide.

[0035] The reception unit can analyze the child's past question history and select the optimal reception method. The reception unit, for example, analyzes the child's past question history and selects the optimal reception method. For example, the reception unit prioritizes receiving topics that the child has frequently asked about in the past. The reception unit can also prioritize suggesting question formats (audio, text, etc.) that the child has used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the child's past question history. In this way, the reception unit can improve the child's learning efficiency by providing the optimal reception method based on the past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the past question history into the generation AI, which then selects the optimal reception method.

[0036] The reception unit can filter questions based on the child's current learning situation and areas of interest when receiving the questions. For example, the reception unit can filter questions based on the child's current learning situation and areas of interest when receiving the questions. For example, the reception unit can preferentially receive questions related to a topic the child is currently studying. The reception unit can also filter highly relevant questions based on the child's areas of interest. The reception unit can also receive questions of an appropriate level of difficulty according to the child's learning progress. This allows the reception unit to preferentially receive questions according to the child's learning situation and areas of interest, thereby improving the effectiveness of learning. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input data on the child's learning situation and areas of interest into the generation AI, and the generation AI can perform filtering.

[0037] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the child's geographical location information. For example, when receiving a question, the reception unit prioritizes receiving highly relevant questions by taking into account the child's geographical location information. For example, when receiving a question, the reception unit prioritizes receiving questions related to studying when the child is at school. Furthermore, when the child is at home, the reception unit can prioritize receiving questions related to home learning. Furthermore, when the child is in a library, the reception unit can prioritize receiving questions related to research. In this way, the reception unit can prioritize receiving highly relevant questions based on the child's geographical location information, thereby improving the effectiveness of learning. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the child's geographical location information into the generation AI, which can select highly relevant questions.

[0038] The reception unit can analyze the child's social media activity when receiving a question and receive related questions. For example, the reception unit can analyze the child's social media activity when receiving a question and receive related questions. For example, the reception unit can prioritize receiving questions related to topics the child is interested in on social media. The reception unit can also filter questions based on the content of accounts the child follows on social media. The reception unit can also analyze the child's current interests from the child's social media activity and receive related questions. This allows the reception unit to prioritize receiving related questions based on the child's social media activity, thereby improving the effectiveness of learning. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the child's social media activity data into the generation AI, which can select related questions.

[0039] The providing unit can adjust the level of detail of the hint based on the importance of the question when providing a hint. For example, the providing unit can adjust the level of detail of the hint based on the importance of the question when providing a hint. For example, the providing unit can provide a detailed hint for an important question. The providing unit can also provide a concise hint for a general question. The providing unit can also provide a hint that allows a quick answer for a question with high urgency. In this way, the providing unit can provide appropriate learning support by adjusting the level of detail of the hint according to the importance of the question. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input question importance data to the generation AI, which can adjust the level of detail of the hint.

[0040] The providing unit can apply different hint generation algorithms depending on the category of the question when providing a hint. For example, the providing unit can apply different hint generation algorithms depending on the category of the question when providing a hint. For example, the providing unit can provide hints encouraging experiments or observations for questions about science. The providing unit can also provide hints encouraging research into related events or people for questions about history. The providing unit can also provide hints for understanding calculation methods or formulas for questions about mathematics. In this way, the providing unit can provide effective learning support by providing hints according to the category of the question. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input question category data into the generation AI, which can then apply a hint generation algorithm.

[0041] The providing unit can determine the priority of hints based on the time when the question was submitted when providing the hints. For example, the providing unit can determine the priority of hints based on the time when the question was submitted when providing the hints. For example, the providing unit can quickly provide hints for urgent questions. The providing unit can also prioritize detailed hints for important questions. The providing unit can also provide concise hints for general questions. In this way, the providing unit can provide appropriate learning support by adjusting the priority of hints depending on the time when the question was submitted. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input question submission time data into the generation AI, and the generation AI can determine the priority of hints.

[0042] The providing unit can adjust the order of hints based on the relevance of the question when providing hints. For example, the providing unit can adjust the order of hints based on the relevance of the question when providing hints. For example, the providing unit can prioritize providing detailed hints when the relevance of the question is high. Furthermore, the providing unit can also provide concise hints when the relevance of the question is low. Furthermore, the providing unit can adjust the order of hints based on the importance of the question. In this way, the providing unit can provide appropriate learning support by adjusting the order of hints based on the relevance of the question. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input question relevance data to the generation AI, and the generation AI can adjust the order of hints.

[0043] The guide unit can select the optimal guide method by referring to past guide data when providing guidance on how to search. For example, the guide unit can select the optimal guide method by referring to past guide data when providing guidance on how to search. For example, the guide unit suggests the optimal guide method based on guide methods used by the child in the past. The guide unit can also select an effective guide method from the child's past learning history. The guide unit can also analyze past guide data and provide the most efficient guide method. In this way, the guide unit can improve the child's learning efficiency by providing the optimal guide method based on the past guide data. Some or all of the above-mentioned processing in the guide unit may be performed using, or without, a generation AI. For example, the guide unit can input past guide data into the generation AI, which can select the optimal guide method.

[0044] The guide unit can apply different guidance techniques to each question category when providing research guidance. For example, the guide unit can apply different guidance techniques to each question category when providing research guidance. For example, the guide unit can provide guidance encouraging experiments and observations for science-related questions. The guide unit can also provide guidance for researching related events and people for history-related questions. The guide unit can also provide guidance for understanding calculation methods and formulas for mathematics-related questions. In this way, the guide unit can provide effective learning support by providing guidance according to the question category. Some or all of the above-mentioned processing in the guide unit can be performed using, or without, a generation AI. For example, the guide unit can input question category data into the generation AI, which can then apply a guidance technique.

[0045] The guide unit can adjust the order of the guides based on the time when the questions were submitted when providing guidance on how to research. For example, the guide unit can adjust the order of the guides based on the time when the questions were submitted when providing guidance on how to research. For example, the guide unit can quickly provide guidance for questions with high urgency. The guide unit can also prioritize providing detailed guidance for important questions. The guide unit can also provide concise guidance for general questions. In this way, the guide unit can provide appropriate learning support by adjusting the order of the guides based on the time when the questions were submitted. Some or all of the above-mentioned processing in the guide unit may be performed using, or without, a generation AI. For example, the guide unit can input question submission time data into the generation AI, and the generation AI can adjust the order of the guides.

[0046] The guide unit can improve the accuracy of the guidance by referring to related literature and materials when providing guidance on how to research. For example, the guide unit can improve the accuracy of the guidance by referring to related literature and materials when providing guidance on how to research. For example, the guide unit can refer to related literature and provide detailed guidance. The guide unit can also suggest specific research methods based on the materials. The guide unit can also analyze literature and materials and provide the most effective guidance method. In this way, the guide unit can provide effective learning support by improving the accuracy of the guidance based on related literature and materials. Some or all of the above-mentioned processing in the guide unit may be performed using, or without, a generation AI. For example, the guide unit can input related literature and material data into the generation AI, which can improve the accuracy of the guidance.

[0047] The feedback unit can select an optimal feedback method by referring to past feedback data when providing feedback. For example, the feedback unit can select an optimal feedback method by referring to past feedback data when providing feedback. For example, the feedback unit can suggest an optimal feedback method based on feedback the child has received in the past. The feedback unit can also select an effective feedback method from the child's past learning history. The feedback unit can also analyze past feedback data and provide the most efficient feedback method. In this way, the feedback unit can improve the child's learning efficiency by providing an optimal feedback method based on past feedback data. Some or all of the above-described processing in the feedback unit can be performed using, or without, a generation AI. For example, the feedback unit can input past feedback data into the generation AI, which can select an optimal feedback method.

[0048] The feedback unit can apply different feedback methods to different question categories when providing feedback. For example, the feedback unit can apply different feedback methods to different question categories when providing feedback. For example, the feedback unit can provide feedback encouraging experimentation or observation for a question about science. The feedback unit can also provide feedback encouraging research into related events or people for a question about history. The feedback unit can also provide feedback encouraging understanding of calculation methods or formulas for a question about mathematics. In this way, the feedback unit can provide effective learning support by providing feedback according to the question category. Some or all of the above-mentioned processing in the feedback unit can be performed using, or without, a generation AI. For example, the feedback unit can input question category data into the generation AI, which can then apply the feedback method.

[0049] The feedback unit can adjust the order of feedback based on the time when the questions were submitted when providing feedback. For example, the feedback unit can adjust the order of feedback based on the time when the questions were submitted when providing feedback. For example, the feedback unit can provide quick feedback for urgent questions. The feedback unit can also prioritize providing detailed feedback for important questions. The feedback unit can also provide concise feedback for general questions. In this way, the feedback unit can provide appropriate learning support by adjusting the order of feedback depending on the time when the questions were submitted. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the feedback unit can input data on the time when the questions were submitted to the generation AI, and the generation AI can adjust the order of feedback.

[0050] The feedback unit can improve the accuracy of the feedback by referring to related literature and materials when providing feedback. For example, the feedback unit can improve the accuracy of the feedback by referring to related literature and materials when providing feedback. For example, the feedback unit can refer to related literature and provide detailed feedback. The feedback unit can also suggest specific feedback based on the materials. The feedback unit can also analyze literature and materials and provide the most effective feedback method. In this way, the feedback unit can provide effective learning support by improving the accuracy of the feedback based on related literature and materials. Some or all of the above-mentioned processing in the feedback unit can be performed using, or without, a generation AI. For example, the feedback unit can input related literature and material data into the generation AI, which can improve the accuracy of the feedback.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The reception unit can analyze the child's learning history and optimize the reception of questions based on the past learning content. For example, the reception unit can prioritize receiving questions related to topics that the child has studied in the past. The reception unit can also identify areas in which the child is weak from the past learning history and prioritize receiving questions related to those areas. Furthermore, the reception unit can suggest questions related to new topics that the child may be interested in based on the past learning history. In this way, the reception unit can utilize the child's learning history to provide more effective learning support.

[0053] When providing hints for questions, the providing unit can adjust the format of the hints according to the child's learning style. For example, for visual learners, hints using diagrams or illustrations can be provided. For auditory learners, audio hints can also be provided. Furthermore, for experiential learners, hints that allow the child to try things out by actually doing them can also be provided. In this way, the providing unit can provide more effective learning support by providing hints according to the child's learning style.

[0054] The guide unit can adjust the guidance method depending on the device used by the child when researching. For example, when using a tablet, it can provide guidance suitable for touch operation. When using a PC, it can also provide guidance suitable for keyboard and mouse operation. Furthermore, when using a smartphone, it can also provide guidance suitable for the screen size. In this way, the guide unit can provide guidance suitable for the device used by the child, thereby providing more effective learning support.

[0055] When providing feedback on the child's research results, the feedback unit can adjust the content of the feedback based on the child's learning goals. For example, for short-term goals, feedback indicating specific areas for improvement can be provided. For long-term goals, feedback evaluating overall progress can be provided. Furthermore, if the goal is to improve a specific skill, feedback related to that skill can be provided. This allows the feedback unit to provide more effective learning support by providing feedback according to the child's learning goals.

[0056] When providing hints for questions, the providing unit can adjust the difficulty of the hints based on the child's learning progress. For example, basic hints can be provided in the early stages of learning. As the child's learning progresses, more advanced hints can be provided. Furthermore, as the child's understanding of a particular topic deepens, more practical hints related to that topic can be provided. This allows the providing unit to provide hints according to the child's learning progress, thereby providing more effective learning support.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit accepts a question when a child inputs it. For example, if a child inputs a question such as "How do plants grow?", the reception unit accepts the question. Step 2: The information provider does not provide a direct answer to the question, but rather provides hints on how to think about or research the question. For example, the information provider might give a hint such as, "Think about what plants need to grow. It involves water, light, soil, etc." Step 3: The guide section guides children on how to research on their own. For example, the guide section may teach specific research methods, such as, "Look for books about plants at the library. You could also try searching for 'plant growth conditions' on the Internet." Step 4: When the child reports the results of their own research to the AI, the feedback section provides feedback on the content. For example, the feedback section might say, "You've done a good job of researching. You've discovered that water and light are important for plant growth. Next, let's find out how much water is needed."

[0059] (Example 2) The educational support system according to an embodiment of the present invention is a children's version of a generative AI system designed to develop children's ability to think independently. This system aims to help children develop the ability to solve problems independently by clearly explaining ways of thinking and researching, rather than directly teaching them the answers. For example, it includes a question acceptance function, a hint provision function, a research guidance function, and a feedback function. With the question acceptance function, a child inputs a question, and the AI ​​accepts the question. For example, suppose the question is, "How do plants grow?" Next, with the hint provision function, the AI ​​does not provide a direct answer to the question, but instead provides hints on how to think and research. For example, it might provide a hint such as, "Think about what plants need to grow. Water, light, soil, etc. are involved." With the research guidance function, the AI ​​guides children on how to research independently. For example, it might teach them specific research methods, such as, "Look for books about plants at the library. You could also try searching for 'plant growth conditions' on the Internet." With the feedback function, a child reports the results of their own research to the AI, and the AI ​​provides feedback on the content. For example, it provides feedback such as, "You've done a good job of researching. You've learned that water and light are important for plant growth. Next, let's find out how much water is needed." In this way, the children's version of generative AI promotes independent learning by supporting children's processes of thinking, researching, and learning on their own. In addition, the AI ​​adjusts the content of hints and research guides according to each child's level of understanding, allowing it to provide support that matches each child's learning pace. In this way, the educational support system supports children's processes of thinking, researching, and learning on their own, promoting independent learning.

[0060] An education support system according to an embodiment includes a reception unit, a provision unit, a guide unit, and a feedback unit. The reception unit receives a question input by a child. For example, if a child inputs a question such as, "How do plants grow?", the reception unit receives the question. The provision unit does not provide a direct answer to the question, but provides hints on how to think about and research the question. For example, the provision unit may provide a hint such as, "Think about what plants need to grow. Water, light, soil, etc. are involved." The guide unit guides the child in how to research the question on their own. For example, the guide unit may provide specific research instructions such as, "Look for a book about plants at the library. You could also try searching for 'plant growth conditions' on the Internet." When the child reports the results of their research to the AI, the feedback unit provides feedback on the content. For example, the feedback unit may provide feedback such as, "You did a good job of researching. You now know that water and light are important for plant growth. Next, let's find out how much water and light are needed." This allows the education support system according to an embodiment to support children's processes of thinking, researching, and learning on their own, thereby promoting independent learning.

[0061] The providing unit may provide hints on how to think about or research questions, rather than providing direct answers to questions. For example, the providing unit may provide hints on how to think about or research questions, rather than providing direct answers to questions. For example, the providing unit may give a hint such as, "Think about what plants need to grow. Water, light, soil, etc. are involved." The providing unit may also analyze questions and generate appropriate hints. For example, the providing unit may analyze the content of the question and generate hints based on related information. This allows the providing unit to develop children's ability to solve problems on their own. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit may input a question into a generation AI, which then generates a hint.

[0062] The guide unit can guide children on how to do their own research. For example, the guide unit can guide children on how to do their own research. For example, the guide unit can teach specific research methods, such as, "Look for a book about plants at the library. You could also try searching for 'plant growth conditions' on the Internet." The guide unit can also guide children on how to find books at the library and how to search on the Internet. For example, the guide unit can teach children how to use classification numbers and how to read the table of contents to find books at the library. The guide unit can also teach children how to use search engines and how to select keywords to search on the Internet. In this way, the guide unit can cultivate children's ability to do their own research. Some or all of the above-mentioned processing in the guide unit may be performed using, or without, a generation AI. For example, the guide unit can input a research guide into the generation AI, which then generates the guide.

[0063] The feedback unit can provide feedback to the child based on the results of their own research. For example, when a child reports the results of their own research to the AI, the feedback unit provides feedback on the content. For example, the feedback unit may provide feedback such as, "You did a good job of researching. You learned that water and light are important for plant growth. Next, let's find out how much water is needed." The feedback unit can also estimate the child's emotions and adjust the content of the feedback based on the estimated child's emotions. For example, the feedback unit can provide simple and easy-to-understand feedback if the child is excited. The feedback unit can also provide feedback with detailed explanations if the child is relaxed. The feedback unit can also provide short and to-the-point feedback if the child is tired. This allows the feedback unit to confirm what the child has learned and obtain feedback to move on to the next step. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit can input the results of the research into the generation AI, which then generates feedback.

[0064] The providing unit can analyze the question and generate an appropriate hint. The providing unit, for example, analyzes the question and generates an appropriate hint. For example, the providing unit analyzes the content of the question and generates a hint based on related information. The providing unit can also use natural language processing technology or keyword extraction technology as a question analysis method. For example, the providing unit can analyze the meaning of the question using natural language processing technology and extract related information. The providing unit can also extract important keywords in the question using keyword extraction technology and generate a hint based on the keywords. In this way, the providing unit can deepen children's understanding by providing appropriate hints for the question. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the providing unit can input a question to a generation AI, which can generate a hint.

[0065] The guide unit can provide guidance on how to find books in a library and how to search the Internet. The guide unit provides guidance on, for example, how to find books in a library and how to search the Internet. For example, the guide unit teaches how to use classification numbers and how to read the table of contents as ways to find books in a library. The guide unit also teaches how to use a search engine and how to select keywords as ways to search the Internet. In this way, the guide unit allows children to learn specific research methods. Some or all of the above-mentioned processing in the guide unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the guide unit can input a guide on how to search into a generation AI, which then generates a guide.

[0066] The reception unit can estimate the child's emotions and adjust the timing of accepting questions based on the estimated child's emotions. The reception unit, for example, estimates the child's emotions and adjusts the timing of accepting questions based on the estimated child's emotions. For example, if the reception unit is excited, it temporarily delays accepting questions and waits until the child calms down. Furthermore, if the child is concentrating, the reception unit can immediately accept questions to prevent interruption of the child's learning flow. Furthermore, if the child is tired, the reception unit can temporarily stop accepting questions and encourage the child to take a break. This allows the reception unit to accept questions at an appropriate time according to the child's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit can input the child's emotion data into the generation AI, which can then estimate the emotion.

[0067] The reception unit can analyze the child's past question history and select the optimal reception method. The reception unit, for example, analyzes the child's past question history and selects the optimal reception method. For example, the reception unit prioritizes receiving topics that the child has frequently asked about in the past. The reception unit can also prioritize suggesting question formats (audio, text, etc.) that the child has used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the child's past question history. In this way, the reception unit can improve the child's learning efficiency by providing the optimal reception method based on the past question history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the past question history into the generation AI, which then selects the optimal reception method.

[0068] The reception unit can filter questions based on the child's current learning situation and areas of interest when receiving the questions. For example, the reception unit can filter questions based on the child's current learning situation and areas of interest when receiving the questions. For example, the reception unit can preferentially receive questions related to a topic the child is currently studying. The reception unit can also filter highly relevant questions based on the child's areas of interest. The reception unit can also receive questions of an appropriate level of difficulty according to the child's learning progress. This allows the reception unit to preferentially receive questions according to the child's learning situation and areas of interest, thereby improving the effectiveness of learning. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input data on the child's learning situation and areas of interest into the generation AI, and the generation AI can perform filtering.

[0069] The reception unit can estimate the child's emotions and determine the priority of questions to be received based on the estimated child's emotions. The reception unit, for example, estimates the child's emotions and determines the priority of questions to be received based on the estimated child's emotions. For example, if the child is excited, the reception unit can prioritize easy questions. Also, if the child is relaxed, the reception unit can prioritize difficult questions. Also, if the child is tired, the reception unit can temporarily stop receiving questions to encourage the child to take a break. In this way, the reception unit can provide an appropriate learning environment by adjusting the priority of questions according to the child's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit can input the child's emotion data into the generation AI, which can estimate the emotion and determine the priority of questions.

[0070] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the child's geographical location information. For example, when receiving a question, the reception unit prioritizes receiving highly relevant questions by taking into account the child's geographical location information. For example, when receiving a question, the reception unit prioritizes receiving questions related to studying when the child is at school. Furthermore, when the child is at home, the reception unit can prioritize receiving questions related to home learning. Furthermore, when the child is in a library, the reception unit can prioritize receiving questions related to research. In this way, the reception unit can prioritize receiving highly relevant questions based on the child's geographical location information, thereby improving the effectiveness of learning. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the child's geographical location information into the generation AI, which can select highly relevant questions.

[0071] The reception unit can analyze the child's social media activity when receiving a question and receive related questions. For example, the reception unit can analyze the child's social media activity when receiving a question and receive related questions. For example, the reception unit can prioritize receiving questions related to topics the child is interested in on social media. The reception unit can also filter questions based on the content of accounts the child follows on social media. The reception unit can also analyze the child's current interests from the child's social media activity and receive related questions. This allows the reception unit to prioritize receiving related questions based on the child's social media activity, thereby improving the effectiveness of learning. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the child's social media activity data into the generation AI, which can select related questions.

[0072] The providing unit can estimate the child's emotion and adjust the way the hint is expressed based on the estimated emotion. For example, the providing unit can estimate the child's emotion and adjust the way the hint is expressed based on the estimated emotion. For example, if the child is excited, the providing unit can provide a simple and easy-to-understand hint. If the child is relaxed, the providing unit can provide a hint with a detailed explanation. If the child is tired, the providing unit can provide a short and to-the-point hint. This allows the providing unit to adjust the way the hint is expressed based on the child's emotion, thereby providing more effective learning support. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the child's emotion data into the generation AI, which can estimate the emotion and adjust the way the hint is expressed.

[0073] The providing unit can adjust the level of detail of the hint based on the importance of the question when providing a hint. For example, the providing unit can adjust the level of detail of the hint based on the importance of the question when providing a hint. For example, the providing unit can provide a detailed hint for an important question. The providing unit can also provide a concise hint for a general question. The providing unit can also provide a hint that allows a quick answer for a question with high urgency. In this way, the providing unit can provide appropriate learning support by adjusting the level of detail of the hint according to the importance of the question. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input question importance data to the generation AI, which can adjust the level of detail of the hint.

[0074] The providing unit can apply different hint generation algorithms depending on the category of the question when providing a hint. For example, the providing unit can apply different hint generation algorithms depending on the category of the question when providing a hint. For example, the providing unit can provide hints encouraging experiments or observations for questions about science. The providing unit can also provide hints encouraging research into related events or people for questions about history. The providing unit can also provide hints for understanding calculation methods or formulas for questions about mathematics. In this way, the providing unit can provide effective learning support by providing hints according to the category of the question. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input question category data into the generation AI, which can then apply a hint generation algorithm.

[0075] The providing unit can estimate the child's emotion and adjust the length of the hint based on the estimated emotion. The providing unit, for example, estimates the child's emotion and adjusts the length of the hint based on the estimated emotion. For example, if the child is excited, the providing unit can provide a short and to-the-point hint. Furthermore, if the child is relaxed, the providing unit can provide a longer hint with detailed explanations. Furthermore, if the child is tired, the providing unit can provide a concise and easy-to-understand hint. This allows the providing unit to adjust the length of the hint according to the child's emotion, thereby providing appropriate learning support. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI. For example, the providing unit can input the child's emotion data into the generation AI, which can estimate the emotion and adjust the length of the hint.

[0076] The providing unit can determine the priority of hints based on the time when the question was submitted when providing the hints. For example, the providing unit can determine the priority of hints based on the time when the question was submitted when providing the hints. For example, the providing unit can quickly provide hints for urgent questions. The providing unit can also prioritize detailed hints for important questions. The providing unit can also provide concise hints for general questions. In this way, the providing unit can provide appropriate learning support by adjusting the priority of hints depending on the time when the question was submitted. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input question submission time data into the generation AI, and the generation AI can determine the priority of hints.

[0077] The providing unit can adjust the order of hints based on the relevance of the question when providing hints. For example, the providing unit can adjust the order of hints based on the relevance of the question when providing hints. For example, the providing unit can prioritize providing detailed hints when the relevance of the question is high. Furthermore, the providing unit can also provide concise hints when the relevance of the question is low. Furthermore, the providing unit can adjust the order of hints based on the importance of the question. In this way, the providing unit can provide appropriate learning support by adjusting the order of hints based on the relevance of the question. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input question relevance data to the generation AI, and the generation AI can adjust the order of hints.

[0078] The guide unit can estimate the child's emotions and adjust the guidance method for how to search based on the estimated child's emotions. For example, the guide unit can estimate the child's emotions and adjust the guidance method for how to search based on the estimated child's emotions. For example, if the child is excited, the guide unit can provide simple and easy-to-understand guidance. If the child is relaxed, the guide unit can provide guidance with detailed explanations. If the child is tired, the guide unit can provide short and to-the-point guidance. This allows the guide unit to adjust the guidance method for how to search based on the child's emotions, thereby providing appropriate learning support. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the guide unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the guide unit can input the child's emotion data into the generation AI, which can estimate the emotion and adjust the guidance method.

[0079] The guide unit can select the optimal guide method by referring to past guide data when providing guidance on how to search. For example, the guide unit can select the optimal guide method by referring to past guide data when providing guidance on how to search. For example, the guide unit suggests the optimal guide method based on guide methods used by the child in the past. The guide unit can also select an effective guide method from the child's past learning history. The guide unit can also analyze past guide data and provide the most efficient guide method. In this way, the guide unit can improve the child's learning efficiency by providing the optimal guide method based on the past guide data. Some or all of the above-mentioned processing in the guide unit may be performed using, or without, a generation AI. For example, the guide unit can input past guide data into the generation AI, which can select the optimal guide method.

[0080] The guide unit can apply different guidance techniques to each question category when providing research guidance. For example, the guide unit can apply different guidance techniques to each question category when providing research guidance. For example, the guide unit can provide guidance encouraging experiments and observations for science-related questions. The guide unit can also provide guidance for researching related events and people for history-related questions. The guide unit can also provide guidance for understanding calculation methods and formulas for mathematics-related questions. In this way, the guide unit can provide effective learning support by providing guidance according to the question category. Some or all of the above-mentioned processing in the guide unit can be performed using, or without, a generation AI. For example, the guide unit can input question category data into the generation AI, which can then apply a guidance technique.

[0081] The guide unit can estimate the child's emotions and determine the priority of search methods based on the estimated child's emotions. The guide unit, for example, estimates the child's emotions and determines the priority of search methods based on the estimated child's emotions. For example, if the child is excited, the guide unit can prioritize providing simple search methods. Also, if the child is relaxed, the guide unit can prioritize providing detailed search methods. Also, if the child is tired, the guide unit can provide simple and easy-to-understand search methods. This allows the guide unit to adjust the priority of search methods according to the child's emotions and provide appropriate learning support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the guide unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the guide unit can input the child's emotion data into the generation AI, which can estimate the emotions and determine the priority of search methods.

[0082] The guide unit can adjust the order of the guides based on the time when the questions were submitted when providing guidance on how to research. For example, the guide unit can adjust the order of the guides based on the time when the questions were submitted when providing guidance on how to research. For example, the guide unit can quickly provide guidance for questions with high urgency. The guide unit can also prioritize providing detailed guidance for important questions. The guide unit can also provide concise guidance for general questions. In this way, the guide unit can provide appropriate learning support by adjusting the order of the guides based on the time when the questions were submitted. Some or all of the above-mentioned processing in the guide unit may be performed using, or without, a generation AI. For example, the guide unit can input question submission time data into the generation AI, and the generation AI can adjust the order of the guides.

[0083] The guide unit can improve the accuracy of the guidance by referring to related literature and materials when providing guidance on how to research. For example, the guide unit can improve the accuracy of the guidance by referring to related literature and materials when providing guidance on how to research. For example, the guide unit can refer to related literature and provide detailed guidance. The guide unit can also suggest specific research methods based on the materials. The guide unit can also analyze literature and materials and provide the most effective guidance method. In this way, the guide unit can provide effective learning support by improving the accuracy of the guidance based on related literature and materials. Some or all of the above-mentioned processing in the guide unit may be performed using, or without, a generation AI. For example, the guide unit can input related literature and material data into the generation AI, which can improve the accuracy of the guidance.

[0084] The feedback unit can estimate the child's emotions and adjust the content of the feedback based on the estimated child's emotions. For example, the feedback unit can estimate the child's emotions and adjust the content of the feedback based on the estimated child's emotions. For example, if the child is excited, the feedback unit can provide simple and easy-to-understand feedback. If the child is relaxed, the feedback unit can provide feedback with detailed explanations. If the child is tired, the feedback unit can provide short and to-the-point feedback. This allows the feedback unit to adjust the content of the feedback according to the child's emotions and provide appropriate learning support. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the feedback unit can input the child's emotion data into the generation AI, which can estimate the emotion and adjust the content of the feedback.

[0085] The feedback unit can select an optimal feedback method by referring to past feedback data when providing feedback. For example, the feedback unit can select an optimal feedback method by referring to past feedback data when providing feedback. For example, the feedback unit can suggest an optimal feedback method based on feedback the child has received in the past. The feedback unit can also select an effective feedback method from the child's past learning history. The feedback unit can also analyze past feedback data and provide the most efficient feedback method. In this way, the feedback unit can improve the child's learning efficiency by providing an optimal feedback method based on past feedback data. Some or all of the above-described processing in the feedback unit can be performed using, or without, a generation AI. For example, the feedback unit can input past feedback data into the generation AI, which can select an optimal feedback method.

[0086] The feedback unit can apply different feedback methods to different question categories when providing feedback. For example, the feedback unit can apply different feedback methods to different question categories when providing feedback. For example, the feedback unit can provide feedback encouraging experimentation or observation for a question about science. The feedback unit can also provide feedback encouraging research into related events or people for a question about history. The feedback unit can also provide feedback encouraging understanding of calculation methods or formulas for a question about mathematics. In this way, the feedback unit can provide effective learning support by providing feedback according to the question category. Some or all of the above-mentioned processing in the feedback unit can be performed using, or without, a generation AI. For example, the feedback unit can input question category data into the generation AI, which can then apply the feedback method.

[0087] The feedback unit can estimate the child's emotions and determine the priority of feedback based on the estimated child's emotions. The feedback unit, for example, estimates the child's emotions and determines the priority of feedback based on the estimated child's emotions. For example, the feedback unit can prioritize providing simple feedback when the child is excited. The feedback unit can also prioritize providing detailed feedback when the child is relaxed. The feedback unit can also provide concise and easy-to-understand feedback when the child is tired. This allows the feedback unit to provide appropriate learning support by adjusting the priority of feedback according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the feedback unit can input the child's emotion data into the generation AI, which can estimate the emotions and determine the priority of feedback.

[0088] The feedback unit can adjust the order of feedback based on the time when the questions were submitted when providing feedback. For example, the feedback unit can adjust the order of feedback based on the time when the questions were submitted when providing feedback. For example, the feedback unit can provide quick feedback for urgent questions. The feedback unit can also prioritize providing detailed feedback for important questions. The feedback unit can also provide concise feedback for general questions. In this way, the feedback unit can provide appropriate learning support by adjusting the order of feedback depending on the time when the questions were submitted. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the feedback unit can input data on the time when the questions were submitted to the generation AI, and the generation AI can adjust the order of feedback.

[0089] The feedback unit can improve the accuracy of the feedback by referring to related literature and materials when providing feedback. For example, the feedback unit can improve the accuracy of the feedback by referring to related literature and materials when providing feedback. For example, the feedback unit can refer to related literature and provide detailed feedback. The feedback unit can also suggest specific feedback based on the materials. The feedback unit can also analyze literature and materials and provide the most effective feedback method. In this way, the feedback unit can provide effective learning support by improving the accuracy of the feedback based on related literature and materials. Some or all of the above-mentioned processing in the feedback unit can be performed using, or without, a generation AI. For example, the feedback unit can input related literature and material data into the generation AI, which can improve the accuracy of the feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, provision unit, guide unit, and feedback unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and accepts a question input by a child. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides hints on how to think about things and how to research. The guide unit is realized, for example, by the control unit 46A of the smart device 14 and guides the child on how to research things on their own. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback on the results of the child's research on their own. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, provision unit, guide unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a question input by a child. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides hints on how to think about things and how to research. The guide unit is realized, for example, by the control unit 46A of the smart glasses 214 and guides the child on how to research things on their own. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback on the results of the child's research on their own. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, provision unit, guide unit, and feedback unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives a question input by the child. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides hints on how to think about things and how to research. The guide unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and guides the child on how to research things on their own. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback on the results of the child's research on their own. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, provision unit, guide unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives a question input by a child. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides hints on how to think about things and how to research. The guide unit is realized, for example, by the control unit 46A of the robot 414 and guides the child on how to research things on their own. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback on the results of the child's research on their own.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The reception unit can analyze the child's learning history and optimize the reception of questions based on the past learning content. For example, the reception unit can prioritize receiving questions related to topics that the child has studied in the past. The reception unit can also identify areas in which the child is weak from the past learning history and prioritize receiving questions related to those areas. Furthermore, the reception unit can suggest questions related to new topics that the child may be interested in based on the past learning history. In this way, the reception unit can utilize the child's learning history to provide more effective learning support.

[0092] When providing hints for questions, the providing unit can adjust the format of the hints according to the child's learning style. For example, for visual learners, hints using diagrams or illustrations can be provided. For auditory learners, audio hints can also be provided. Furthermore, for experiential learners, hints that allow the child to try things out by actually doing them can also be provided. In this way, the providing unit can provide more effective learning support by providing hints according to the child's learning style.

[0093] The guide unit can adjust the guidance method depending on the device used by the child when researching. For example, when using a tablet, it can provide guidance suitable for touch operation. When using a PC, it can also provide guidance suitable for keyboard and mouse operation. Furthermore, when using a smartphone, it can also provide guidance suitable for the screen size. In this way, the guide unit can provide guidance suitable for the device used by the child, thereby providing more effective learning support.

[0094] When providing feedback on the child's research results, the feedback unit can adjust the content of the feedback based on the child's learning goals. For example, for short-term goals, feedback indicating specific areas for improvement can be provided. For long-term goals, feedback evaluating overall progress can be provided. Furthermore, if the goal is to improve a specific skill, feedback related to that skill can be provided. This allows the feedback unit to provide more effective learning support by providing feedback according to the child's learning goals.

[0095] When providing hints for questions, the providing unit can adjust the difficulty of the hints based on the child's learning progress. For example, basic hints can be provided in the early stages of learning. As the child's learning progresses, more advanced hints can be provided. Furthermore, as the child's understanding of a particular topic deepens, more practical hints related to that topic can be provided. This allows the providing unit to provide hints according to the child's learning progress, thereby providing more effective learning support.

[0096] The reception unit can estimate the child's emotions and adjust the method of receiving questions based on the estimated emotions. For example, if the child is excited, the reception unit can temporarily delay the reception of questions and wait until the child calms down. Also, if the child is concentrating, the reception unit can immediately accept questions to avoid interrupting the flow of learning. Furthermore, if the child is tired, the reception unit can temporarily stop accepting questions and encourage the child to take a break. This allows the reception unit to accept questions at an appropriate time according to the child's emotions.

[0097] The providing unit can estimate the child's emotions and adjust the way in which hints are expressed based on the estimated emotions. For example, if the child is excited, a simple and easy-to-understand hint can be provided. If the child is relaxed, a hint including a detailed explanation can be provided. Furthermore, if the child is tired, a short and to-the-point hint can be provided. In this way, the providing unit can provide more effective learning support by adjusting the way in which hints are expressed according to the child's emotions.

[0098] The guide unit can estimate the child's emotions and adjust the guidance method for how to search based on the estimated emotions. For example, if the child is excited, a simple and easy-to-understand guide can be provided. If the child is relaxed, a guide including detailed explanations can be provided. Furthermore, if the child is tired, a short and to-the-point guide can be provided. In this way, the guide unit can provide appropriate learning support by adjusting the guidance method for how to search based on the child's emotions.

[0099] The feedback unit can estimate the child's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the child is excited, simple and easy-to-understand feedback can be provided. If the child is relaxed, feedback including detailed explanations can be provided. Furthermore, if the child is tired, short and to the point feedback can be provided. In this way, the feedback unit can provide appropriate learning support by adjusting the content of the feedback according to the child's emotions.

[0100] The providing unit can estimate the child's emotion and adjust the length of the hint based on the estimated emotion. For example, if the child is excited, a short and to-the-point hint can be provided. If the child is relaxed, a longer hint including detailed explanations can be provided. Furthermore, if the child is tired, a concise and easy-to-understand hint can be provided. In this way, the providing unit can provide appropriate learning support by adjusting the length of the hint according to the child's emotion.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit accepts a question when a child inputs it. For example, if a child inputs a question such as "How do plants grow?", the reception unit accepts the question. Step 2: The information provider does not provide a direct answer to the question, but rather provides hints on how to think about or research the question. For example, the information provider might give a hint such as, "Think about what plants need to grow. It involves water, light, soil, etc." Step 3: The guide section guides children on how to research on their own. For example, the guide section may teach specific research methods, such as, "Look for books about plants at the library. You could also try searching for 'plant growth conditions' on the Internet." Step 4: When the child reports the results of their own research to the AI, the feedback section provides feedback on the content. For example, the feedback section might say, "You've done a good job of researching. You've discovered that water and light are important for plant growth. Next, let's find out how much water is needed."

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for accepting questions; a providing unit that provides a hint based on the question received by the receiving unit; a guide unit that provides guidance on how to investigate based on the hints provided by the providing unit; a feedback unit that provides feedback on the results of the investigation based on the investigation method guided by the guide unit. A system characterized by:

2. The providing unit Rather than providing a direct answer to a question, provide hints on how to think about it or how to research it.

2. The system of claim 1.

3. The guide portion is Guide your child in their own research 2. The system of claim 1.

4. The feedback unit Providing feedback on children's findings 2. The system of claim 1.

5. The providing unit Parse the question and generate appropriate hints 2. The system of claim 1.

6. The guide portion is Guides on how to find books in the library and on the internet 2. The system of claim 1.

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

2. The system of claim 1.

8. The reception unit Analyze the child's past question history and select the most appropriate reception method 2. The system of claim 1.

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