system

Abstract

The system according to this embodiment aims to enable children to access accurate and in-depth knowledge. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a translation unit, a dialogue unit, and a suggestion unit. The acquisition unit acquires highly reliable information. The analysis unit analyzes the information acquired by the acquisition unit. The translation unit translates and summarizes the information analyzed by the analysis unit for children. The dialogue unit provides an interactive learning experience. The suggestion unit analyzes the learning history and suggests new topics.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for children to access accurate and in-depth knowledge and the needs for specialized information cannot be met.

[0005] The system according to the embodiment aims to enable children to access accurate and in-depth knowledge.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a translation unit, a dialogue unit, and a suggestion unit. The acquisition unit acquires highly reliable information. The analysis unit analyzes the information acquired by the acquisition unit. The translation unit translates and summarizes the information analyzed by the analysis unit for children. The dialogue unit provides an interactive learning experience. The suggestion unit analyzes the learning history and suggests new topics. [Effects of the Invention]

[0007] The system according to this embodiment can enable children to access accurate and in-depth knowledge. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. As an example of the communication standard applied to the communication I / F, wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark) can be mentioned.

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent service according to an embodiment of the present invention is a system that supports children's curiosity by utilizing generative AI. This system acquires information from highly reliable academic papers and public institution databases, and the generative AI quickly provides the necessary knowledge, ensuring that the information is always up-to-date and accurate. For example, the AI ​​agent service translates and summarizes complex technical terms in a way that is easy for children to understand, and promotes understanding through explanations using diagrams and animations. Through interactive learning experiences, it provides an environment where children can immediately resolve their questions, analyzes their learning history, and proposes new topics based on their individual interests, thereby continuously fostering their curiosity. Furthermore, by creating an environment where everyone can receive advanced learning support, it realizes equal educational opportunities, and the AI ​​agent's role in learning support reduces the time and effort burden on parents and educators. For example, the AI ​​agent service acquires information from highly reliable academic papers and public institution databases. In this process, the generative AI verifies the reliability of the information and provides accurate information. For example, it extracts necessary information from the latest scientific research and reports from public institutions. Next, the generative AI analyzes the acquired information and translates and summarizes complex technical terms in a way that is easy for children to understand. For example, it replaces specialized scientific terms with simple words to make them easy for children to understand. Furthermore, the system facilitates understanding through visual explanations using diagrams and animations. It also provides an interactive learning experience. When children have questions, they can ask the generative AI, which provides immediate and appropriate answers. For example, in response to a question about a science experiment, the generative AI explains the procedure and results. The generative AI also analyzes learning history and suggests new topics based on individual interests. For instance, a child interested in space will be suggested new information and topics related to space. This continuously fosters children's curiosity. Moreover, the AI ​​agent's learning support reduces the time and effort burden on parents and educators. For example, even if parents don't have time to support their child's learning, the AI ​​agent can provide that support. Thus, this generative AI-powered AI agent service for supporting children's curiosity fosters children's inquisitiveness and contributes to the development of future innovators and leaders.Furthermore, by supporting lifelong learning, we will strengthen the intellectual foundation of society as a whole through improved information literacy. In this way, AI agent services can foster children's curiosity and contribute to the development of future innovators and leaders.

[0029] The AI ​​agent service according to this embodiment comprises an acquisition unit, an analysis unit, a translation unit, a dialogue unit, and a proposal unit. The acquisition unit acquires highly reliable information. For example, the acquisition unit acquires information from highly reliable academic papers or databases of public institutions. The acquisition unit can use generative AI to verify the reliability of the information and provide accurate information. For example, the acquisition unit extracts necessary information from the latest scientific research or reports from public institutions. The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit verifies the reliability of the acquired information and provides accurate information. The analysis unit can use generative AI to analyze the information. For example, the analysis unit verifies the reliability of the information using evaluation criteria and verification methods for information sources. The translation unit translates and summarizes the information analyzed by the analysis unit for children. For example, the translation unit translates and summarizes difficult technical terms in a way that is easy for children to understand. The translation unit can use generative AI to replace specialized scientific terms with simpler words so that children can easily understand them. For example, the translation unit can promote understanding by using diagrams and animations to provide visual explanations. The dialogue unit provides an interactive learning experience. For example, the dialogue unit provides an immediate and appropriate answer when a child asks a question. The dialogue unit can provide immediate answers to children's questions using generative AI. For example, in response to a question about a science experiment, the dialogue unit will explain the procedure and results of the experiment. The suggestion unit analyzes the learning history and proposes new topics. For example, the suggestion unit uses generative AI to analyze the learning history and propose new topics based on individual interests. The suggestion unit can use generative AI to propose new information and topics based on children's interests. For example, the suggestion unit will propose new information and topics related to space to a child who is interested in space. In this way, the AI ​​agent service according to this embodiment can foster children's curiosity and contribute to the development of future innovators and leaders.

[0030] The information acquisition unit acquires highly reliable information. For example, it acquires information from reliable academic papers and public institution databases. Specifically, the acquisition unit accesses academic paper databases and public institution websites to search for the latest research results and official reports. These information sources are important for providing reliable and accurate information. The acquisition unit can use generative AI to verify the reliability of information and provide accurate information. Generative AI checks the source and citations of information and eliminates unreliable information. For example, generative AI evaluates the number of citations and author reliability of academic papers and selects only highly reliable information. Generative AI can also analyze the content of information and detect inconsistencies and errors. This allows the acquisition unit to efficiently collect reliable information and provide accurate information. Furthermore, the acquisition unit can automate the information collection process and acquire the latest information in real time. For example, the acquisition unit periodically scans academic paper databases and public institution websites and automatically acquires new information when it is added. The acquisition unit also centrally manages the collected information and makes it accessible to other departments. This allows the data acquisition unit to collect information efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit verifies the reliability of the acquired information and provides accurate information. Specifically, the analysis unit verifies the reliability of the information using evaluation criteria and verification methods for information sources. The generation AI checks the source and citations of the information and eliminates unreliable information. For example, the generation AI evaluates the number of citations and the reliability of authors in academic papers, selecting only highly reliable information. The generation AI can also analyze the content of the information and detect inconsistencies and errors. This allows the analysis unit to efficiently analyze highly reliable information and provide accurate information. Furthermore, the analysis unit can automate the information analysis process and analyze the latest information in real time. For example, the analysis unit periodically scans acquired information and automatically analyzes new information when it is added. The analysis unit also centrally manages the analysis results and makes them accessible to other departments. This allows the analysis unit to analyze information efficiently and effectively, improving the overall system performance.

[0032] The translation department translates and summarizes information analyzed by the analysis department for children. For example, the translation department translates and summarizes complex technical terms in a way that is easy for children to understand. Specifically, the translation department can use generative AI to replace specialized scientific terms with simpler language, making it easier for children to comprehend. The generative AI analyzes the meaning of technical terms and converts them into appropriate, simple language. It can also extract the key points of information and summarize them concisely. For example, the translation department can facilitate understanding by using diagrams and animations for visual explanations. This allows the translation department to provide information in a way that is engaging and easy for children to understand. Furthermore, the translation department can automate the information translation and summarization process, providing up-to-date information in real time. For example, the translation department periodically scans the analyzed information and automatically translates and summarizes new information as it is added. The translation department also centrally manages the translation and summarization results, making them accessible to other departments. This allows the translation department to translate and summarize information efficiently and effectively, improving the overall system performance.

[0033] The Dialogue Department provides an interactive learning experience. For example, it provides immediate and appropriate answers when a child asks a question. Specifically, the Dialogue Department can provide instant answers to children's questions using generative AI. The generative AI analyzes the content of the question and generates an appropriate answer. For example, in response to a question about a science experiment, the Dialogue Department will explain the procedure and results of the experiment. Furthermore, the Dialogue Department can adjust the content and difficulty level of the answers according to the children's interests and level of understanding. This allows the Dialogue Department to provide information in a way that is interesting and easy for children to understand. In addition, the Dialogue Department can automate the dialogue process and provide up-to-date information in real time. For example, the Dialogue Department periodically scans the content of questions and automatically generates answers when new questions are added. The Dialogue Department also centrally manages the dialogue results and makes them accessible to other departments. This allows the Dialogue Department to provide information efficiently and effectively, improving the overall performance of the system.

[0034] The suggestion department analyzes learning history and proposes new topics. For example, the suggestion department uses a generative AI to analyze learning history and propose new topics based on individual interests. Specifically, the suggestion department can use generative AI to propose new information and topics based on children's interests. The generative AI analyzes learning history and identifies children's interests and concerns. For example, the suggestion department will propose new information and topics related to space to a child who is interested in space. The suggestion department can also evaluate learning progress and understanding based on children's learning history and propose appropriate topics. This allows the suggestion department to provide information in a way that is interesting to children and increases their motivation to learn. Furthermore, the suggestion department can automate the suggestion process and provide the latest information in real time. For example, the suggestion department can periodically scan learning history and automatically generate suggestions when new information is added. The suggestion department also centrally manages the suggestion results and makes them accessible to other departments. This allows the suggestion department to provide information efficiently and effectively and improve the overall performance of the system.

[0035] The data acquisition unit can retrieve information from highly reliable academic papers and public institution databases. For example, the data acquisition unit can retrieve information from highly reliable academic papers and public institution databases. The data acquisition unit can use generative AI to verify the reliability of the information and provide accurate information. For example, the data acquisition unit can extract necessary information from the latest scientific research and public institution reports. This allows for the provision of accurate knowledge by obtaining highly reliable information. Highly reliable academic papers and public institution databases include, but are not limited to, specific academic journals and government agency databases.

[0036] The analysis unit can verify the reliability of acquired information and provide accurate information. For example, the analysis unit can verify the reliability of acquired information and provide accurate information. The analysis unit can perform information analysis using generative AI. For example, the analysis unit can verify the reliability of information using evaluation criteria and verification methods for information sources. This allows for the provision of accurate information by verifying its reliability. Specific methods and criteria for verifying reliability include, but are not limited to, evaluation criteria and verification methods for information sources.

[0037] The translation department can translate and summarize complex technical terms in a way that is easy for children to understand. For example, the translation department can translate and summarize complex technical terms in a way that is easy for children to understand. Using generative AI, the translation department can replace specialized scientific terms with simpler language, making them easier for children to comprehend. For example, the translation department can facilitate understanding by using diagrams and animations for visual explanations. This facilitates children's understanding by translating and summarizing technical terms in an easy-to-understand manner. Specific examples of complex technical terms and translation methods include, but are not limited to, specific technical terms and translation standards.

[0038] The dialogue unit can provide immediate and appropriate answers when children ask questions. For example, the dialogue unit can provide immediate and appropriate answers when children ask questions. Using generative AI, the dialogue unit can provide immediate answers to children's questions. For example, in response to a question about a science experiment, the dialogue unit can explain the procedure and results of the experiment. This provides an environment where children can immediately resolve their questions. Specific criteria and methods for providing appropriate answers include, but are not limited to, accuracy and speed of the answer.

[0039] The suggestion function can analyze learning history and propose new topics based on individual interests. For example, the suggestion function can use a generative AI to analyze learning history and propose new topics based on individual interests. The suggestion function can use the generative AI to propose new information and topics based on children's interests. For example, the suggestion function will propose new information and topics related to space to a child who is interested in space. This continuously fosters a spirit of inquiry by proposing new topics based on individual interests. Specific methods and criteria for identifying individual interests include, but are not limited to, past learning history and survey results.

[0040] The data acquisition unit can analyze the user's past search history and select the optimal data acquisition method. For example, the data acquisition unit can prioritize acquiring information related to topics the user has frequently searched for in the past. The data acquisition unit can automatically acquire relevant information based on search keywords the user has used in the past. The data acquisition unit can acquire information related to a specific time period from the user's past search history. This allows the system to select the optimal information acquisition method by analyzing past search history. Specific criteria and selection methods for the optimal data acquisition method include, but are not limited to, search algorithms and filtering criteria.

[0041] The information retrieval unit can filter information based on the user's current learning status and areas of interest. For example, the unit can prioritize retrieving information related to the topic the user is currently studying. The unit can filter and retrieve highly relevant information based on the user's areas of interest. The unit can retrieve information of appropriate difficulty level according to the user's learning progress. This allows the system to provide highly relevant information by filtering it based on the user's current learning status and areas of interest. Specific criteria and methods for filtering include, but are not limited to, methods for identifying areas of interest and filtering algorithms.

[0042] The data acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring information. For example, if the user is in a specific region, the data acquisition unit can prioritize the acquisition of information related to that region. If the user is traveling, the data acquisition unit can prioritize the acquisition of information related to the travel destination. If the user is participating in a specific event, the data acquisition unit can prioritize the acquisition of information related to that event. In this way, highly relevant information can be provided by considering geographical location. Specific methods and criteria for considering geographical location include, but are not limited to, the method of acquiring location information and the criteria for evaluating relevance.

[0043] The data acquisition unit can analyze a user's social media activity and acquire relevant information when acquiring data. For example, the data acquisition unit can prioritize acquiring information related to topics shared by the user on social media. The data acquisition unit can acquire relevant information based on the content of posts from accounts the user follows. The data acquisition unit can acquire information related to groups and events the user participates in on social media. This allows for the provision of highly relevant information by analyzing social media activity. Specific methods and criteria for analyzing social media activity include, but are not limited to, methods for analyzing post content and criteria for evaluating relevance.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis of important information to promote a deeper understanding. For less important information, the analysis unit performs a concise analysis to efficiently provide the information. The analysis unit can adjust the level of detail of the analysis based on importance according to the user's learning objectives. This allows for efficient information delivery by adjusting the level of detail of the analysis based on the importance of the information. Specific criteria and methods for evaluating the importance of information include, but are not limited to, the reliability and relevance of the information.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a scientific analysis algorithm to scientific information, a historical analysis algorithm to historical information, and an artistic analysis algorithm to artistic information. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of information. Specific types of analysis algorithms and their application methods include, but are not limited to, machine learning algorithms and statistical analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on when the information was acquired. For example, the analysis unit prioritizes the analysis of the latest information and provides it to the user. The analysis unit can analyze older information as needed. The analysis unit can determine the priority of analysis based on when the information was acquired, according to the user's learning objectives. This allows the system to provide the latest information by prioritizing analysis based on when the information was acquired. Specific methods and criteria for considering when the information was acquired include, but are not limited to, the priority of the latest information and the freshness of the information.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information and provide it to the user. The analysis unit can postpone the analysis of less relevant information. The analysis unit can adjust the order of analysis based on the relevance of the information according to the user's learning objectives. This allows for efficient information delivery by adjusting the order of analysis based on the relevance of the information. Specific criteria and methods for evaluating the relevance of information include, but are not limited to, commonalities in information and relevance scoring.

[0048] The translation unit can adjust the level of detail in translations based on the importance of technical terms. For example, it can provide detailed translations of important technical terms to promote deeper understanding, and concise translations of less important technical terms to efficiently deliver information. The translation unit can also adjust the level of detail in translations based on the importance of technical terms according to the user's learning objectives. This allows for efficient information delivery by adjusting the level of detail in translations based on the importance of technical terms. Specific criteria and methods for evaluating the importance of technical terms include, but are not limited to, the frequency of the term and the difficulty of understanding it.

[0049] The translation unit can apply different translation algorithms depending on the category of information during translation. For example, it can apply a scientific translation algorithm to scientific information, a historical translation algorithm to historical information, and an artistic translation algorithm to artistic information. This improves the accuracy of translations by applying the appropriate translation algorithm according to the category of information. Specific types and application methods of translation algorithms include, but are not limited to, machine translation algorithms and rule-based translation.

[0050] The translation department can prioritize translations based on when the information was acquired. For example, the department prioritizes translating the latest information and providing it to the user. The department can translate older information as needed. The department can prioritize translations based on when the information was acquired, according to the user's learning objectives. This allows the department to provide the latest information by prioritizing translations based on when the information was acquired. Specific methods and criteria for considering when the information was acquired include, but are not limited to, the priority of the latest information and the freshness of the information.

[0051] The translation unit can adjust the order of translations based on the relevance of the information during the translation process. For example, the translation unit can prioritize translating highly relevant information and provide it to the user. The translation unit can postpone the translation of less relevant information. The translation unit can adjust the order of translations based on the relevance of the information according to the user's learning objectives. This allows for efficient information delivery by adjusting the order of translations based on the relevance of the information. Specific criteria and methods for evaluating the relevance of information include, but are not limited to, commonalities in information and relevance scoring.

[0052] The dialogue unit can adjust the level of detail in its answers based on the importance of the questions during the conversation. For example, it can provide detailed answers to important questions to promote a deeper understanding. For less important questions, it can provide concise answers to efficiently deliver information. The dialogue unit can adjust the level of detail in its answers based on the importance of the questions according to the user's learning objectives. This allows for efficient information delivery by adjusting the level of detail in answers based on the importance of the questions. Specific criteria and methods for evaluating the importance of questions include, but are not limited to, the frequency and relevance of the questions.

[0053] The dialogue unit can apply different dialogue algorithms depending on the category of the question during a conversation. For example, it can apply a scientific dialogue algorithm to questions about science, a historical dialogue algorithm to questions about history, and an artistic dialogue algorithm to questions about art. This improves the accuracy of the conversation by applying the appropriate dialogue algorithm according to the category of the question. Specific types and application methods of dialogue algorithms include, but are not limited to, natural language processing algorithms and rule-based dialogues.

[0054] The dialogue unit can prioritize answers based on when the questions were submitted during the dialogue. For example, the dialogue unit will prioritize answering the most recent questions and provide them to the user. The dialogue unit can answer older questions as needed. The dialogue unit can prioritize answers based on when the questions were submitted, according to the user's learning objectives. This allows the system to provide up-to-date information by prioritizing answers based on when the questions were submitted. Specific methods and criteria for considering when questions were submitted include, but are not limited to, prioritizing the most recent questions and the freshness of the questions.

[0055] The dialogue unit can adjust the order of answers based on the relevance of the questions during the conversation. For example, the dialogue unit can prioritize answering and providing answers to highly relevant questions to the user. The dialogue unit can postpone answering less relevant questions. The dialogue unit can adjust the order of answers based on the relevance of the questions according to the user's learning objectives. This allows for efficient information delivery by adjusting the order of answers based on the relevance of the questions. Specific criteria and methods for evaluating the relevance of questions include, but are not limited to, commonalities between questions and relevance scoring.

[0056] The suggestion function can adjust the level of detail of suggestions based on the importance of the learning history. For example, for important learning history, the suggestion function can provide detailed suggestions to promote a deeper understanding. For less important learning history, the suggestion function can provide concise suggestions to efficiently deliver information. The suggestion function can adjust the level of detail of suggestions based on the importance of the learning history according to the user's learning objectives. This allows for efficient information delivery by adjusting the level of detail of suggestions based on the importance of the learning history. Specific criteria and methods for evaluating the importance of learning history include, but are not limited to, the frequency and relevance of learning.

[0057] The suggestion function can apply different suggestion algorithms depending on the category of the learning history during the suggestion process. For example, the suggestion function can apply a scientific suggestion algorithm to a learning history related to science, a historical suggestion algorithm to a learning history related to history, and an artistic suggestion algorithm to a learning history related to art. This improves the accuracy of the suggestions by applying the appropriate suggestion algorithm according to the category of the learning history. Specific types of suggestion algorithms and their application methods include, but are not limited to, recommendation algorithms and machine learning algorithms.

[0058] The suggestion team can prioritize suggestions based on the timing of learning history submissions. For example, the suggestion team may prioritize suggesting and providing the most recent learning history to the user. The suggestion team can also suggest older learning history as needed. The suggestion team can prioritize suggestions based on the timing of learning history submissions according to the user's learning objectives. This allows for the provision of up-to-date information by prioritizing suggestions based on the timing of learning history submissions. Specific methods and criteria for considering the timing of learning history submissions include, but are not limited to, the priority given to the most recent learning history and the freshness of the history.

[0059] The suggestion function can adjust the order of suggestions based on the relevance of the learning history. For example, the suggestion function can prioritize suggesting and providing highly relevant learning history to the user. The suggestion function can postpone suggesting less relevant learning history. The suggestion function can adjust the order of suggestions based on the relevance of the learning history according to the user's learning objectives. This allows for efficient information delivery by adjusting the order of suggestions based on the relevance of the learning history. Specific criteria and methods for evaluating the relevance of learning history include, but are not limited to, commonalities in history and relevance scoring.

[0060] The suggestion function can propose new topics based on the user's learning history. For example, the suggestion function can suggest new information related to topics the user has previously studied. The suggestion function can suggest new topics that the user might be interested in based on their learning history. The suggestion function can analyze the user's learning history and suggest topics to learn next. In this way, by suggesting new topics based on the user's learning history, a sense of curiosity is continuously fostered. Specific criteria and methods for suggesting new topics include, but are not limited to, topic selection criteria and suggestion methods.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The information acquisition unit can monitor the user's learning progress in real time and adjust the frequency of information acquisition according to that progress. For example, if the user is progressing smoothly, the frequency of information acquisition can be increased to maintain the learning flow. Conversely, if the user is struggling with learning, the frequency of information acquisition can be reduced to allow time for deeper understanding. The information acquisition unit can also adjust the difficulty level of the next information acquired based on the user's learning progress. This makes it possible to provide information that matches the user's learning pace, thereby improving learning efficiency.

[0063] The analysis unit can analyze the user's learning history and evaluate the reliability of information based on past learning. For example, if a user has frequently used highly reliable information sources in the past, it will prioritize analyzing information from those sources. Conversely, if a user has frequently used unreliable information sources, it will carefully analyze information from those sources. The analysis unit can also re-evaluate the reliability of specific information sources based on the user's learning history and adjust the analysis algorithm as needed. This enables reliability evaluation based on the user's learning history, resulting in the provision of more accurate information.

[0064] The translation department can customize the translation method according to the user's learning style. For example, users who prefer visual learning can be provided with translations that make extensive use of diagrams and animations. Users who prefer auditory learning can have audio explanations added. Furthermore, users who prefer tactile learning can be provided with translations that incorporate interactive elements. This allows for translations tailored to the user's learning style, leading to a deeper understanding.

[0065] The dialogue unit can provide answers based on the user's learning history, referencing previously asked questions. For example, if a user has asked the same question before, it can reuse that answer to provide a quick response. It can also provide additional relevant information based on past questions. This enables efficient dialogue utilizing the user's learning history, thereby improving the quality of learning.

[0066] The suggestion function can analyze the user's learning history and suggest new topics according to their learning progress. For example, if a user has a solid understanding of a particular topic, it will suggest more advanced content related to that topic. Conversely, if their understanding is insufficient, it will suggest basic content again. The suggestion function can also visualize the user's learning progress based on their learning history and visually present the next topic they should learn. This enables appropriate suggestions tailored to the user's learning progress and helps maintain their motivation to learn.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The acquisition unit acquires highly reliable information. For example, it acquires information from reliable academic papers and public institution databases, verifies the reliability of the information using generation AI, and provides accurate information. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. For example, it verifies the reliability of the acquired information and performs analysis of the information using a generation AI. Step 3: The translation unit translates and summarizes the information analyzed by the analysis unit for children. For example, it translates and summarizes complex technical terms in a way that is easy for children to understand, uses generative AI to replace specialized scientific terms with simpler language, and explains them visually using diagrams and animations. Step 4: The dialogue section provides an interactive learning experience. For example, it provides an immediate and appropriate answer when a child asks a question, using generative AI to instantly answer children's questions. Step 5: The suggestion department analyzes the learning history and proposes new topics. For example, a generating AI analyzes the learning history and proposes new topics based on individual interests.

[0069] (Example of form 2) The AI ​​agent service according to an embodiment of the present invention is a system that supports children's curiosity by utilizing generative AI. This system acquires information from highly reliable academic papers and public institution databases, and the generative AI quickly provides the necessary knowledge, ensuring that the information is always up-to-date and accurate. For example, the AI ​​agent service translates and summarizes complex technical terms in a way that is easy for children to understand, and promotes understanding through explanations using diagrams and animations. Through interactive learning experiences, it provides an environment where children can immediately resolve their questions, analyzes their learning history, and proposes new topics based on their individual interests, thereby continuously fostering their curiosity. Furthermore, by creating an environment where everyone can receive advanced learning support, it realizes equal educational opportunities, and the AI ​​agent's role in learning support reduces the time and effort burden on parents and educators. For example, the AI ​​agent service acquires information from highly reliable academic papers and public institution databases. In this process, the generative AI verifies the reliability of the information and provides accurate information. For example, it extracts necessary information from the latest scientific research and reports from public institutions. Next, the generative AI analyzes the acquired information and translates and summarizes complex technical terms in a way that is easy for children to understand. For example, it replaces specialized scientific terms with simple words to make them easy for children to understand. Furthermore, the system facilitates understanding through visual explanations using diagrams and animations. It also provides an interactive learning experience. When children have questions, they can ask the generative AI, which provides immediate and appropriate answers. For example, in response to a question about a science experiment, the generative AI explains the procedure and results. The generative AI also analyzes learning history and suggests new topics based on individual interests. For instance, a child interested in space will be suggested new information and topics related to space. This continuously fosters children's curiosity. Moreover, the AI ​​agent's learning support reduces the time and effort burden on parents and educators. For example, even if parents don't have time to support their child's learning, the AI ​​agent can provide that support. Thus, this generative AI-powered AI agent service for supporting children's curiosity fosters children's inquisitiveness and contributes to the development of future innovators and leaders.Furthermore, by supporting lifelong learning, we will strengthen the intellectual foundation of society as a whole through improved information literacy. In this way, AI agent services can foster children's curiosity and contribute to the development of future innovators and leaders.

[0070] The AI ​​agent service according to this embodiment comprises an acquisition unit, an analysis unit, a translation unit, a dialogue unit, and a proposal unit. The acquisition unit acquires highly reliable information. For example, the acquisition unit acquires information from highly reliable academic papers or databases of public institutions. The acquisition unit can use generative AI to verify the reliability of the information and provide accurate information. For example, the acquisition unit extracts necessary information from the latest scientific research or reports from public institutions. The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit verifies the reliability of the acquired information and provides accurate information. The analysis unit can use generative AI to analyze the information. For example, the analysis unit verifies the reliability of the information using evaluation criteria and verification methods for information sources. The translation unit translates and summarizes the information analyzed by the analysis unit for children. For example, the translation unit translates and summarizes difficult technical terms in a way that is easy for children to understand. The translation unit can use generative AI to replace specialized scientific terms with simpler words so that children can easily understand them. For example, the translation unit can promote understanding by using diagrams and animations to provide visual explanations. The dialogue unit provides an interactive learning experience. For example, the dialogue unit provides an immediate and appropriate answer when a child asks a question. The dialogue unit can provide immediate answers to children's questions using generative AI. For example, in response to a question about a science experiment, the dialogue unit will explain the procedure and results of the experiment. The suggestion unit analyzes the learning history and proposes new topics. For example, the suggestion unit uses generative AI to analyze the learning history and propose new topics based on individual interests. The suggestion unit can use generative AI to propose new information and topics based on children's interests. For example, the suggestion unit will propose new information and topics related to space to a child who is interested in space. In this way, the AI ​​agent service according to this embodiment can foster children's curiosity and contribute to the development of future innovators and leaders.

[0071] The information acquisition unit acquires highly reliable information. For example, it acquires information from reliable academic papers and public institution databases. Specifically, the acquisition unit accesses academic paper databases and public institution websites to search for the latest research results and official reports. These information sources are important for providing reliable and accurate information. The acquisition unit can use generative AI to verify the reliability of information and provide accurate information. Generative AI checks the source and citations of information and eliminates unreliable information. For example, generative AI evaluates the number of citations and author reliability of academic papers and selects only highly reliable information. Generative AI can also analyze the content of information and detect inconsistencies and errors. This allows the acquisition unit to efficiently collect reliable information and provide accurate information. Furthermore, the acquisition unit can automate the information collection process and acquire the latest information in real time. For example, the acquisition unit periodically scans academic paper databases and public institution websites and automatically acquires new information when it is added. The acquisition unit also centrally manages the collected information and makes it accessible to other departments. This allows the data acquisition unit to collect information efficiently and effectively, improving the overall performance of the system.

[0072] The analysis unit analyzes the information acquired by the acquisition unit. For example, the analysis unit verifies the reliability of the acquired information and provides accurate information. Specifically, the analysis unit verifies the reliability of the information using evaluation criteria and verification methods for information sources. The generation AI checks the source and citations of the information and eliminates unreliable information. For example, the generation AI evaluates the number of citations and the reliability of authors in academic papers, selecting only highly reliable information. The generation AI can also analyze the content of the information and detect inconsistencies and errors. This allows the analysis unit to efficiently analyze highly reliable information and provide accurate information. Furthermore, the analysis unit can automate the information analysis process and analyze the latest information in real time. For example, the analysis unit periodically scans acquired information and automatically analyzes new information when it is added. The analysis unit also centrally manages the analysis results and makes them accessible to other departments. This allows the analysis unit to analyze information efficiently and effectively, improving the overall system performance.

[0073] The translation department translates and summarizes information analyzed by the analysis department for children. For example, the translation department translates and summarizes complex technical terms in a way that is easy for children to understand. Specifically, the translation department can use generative AI to replace specialized scientific terms with simpler language, making it easier for children to comprehend. The generative AI analyzes the meaning of technical terms and converts them into appropriate, simple language. It can also extract the key points of information and summarize them concisely. For example, the translation department can facilitate understanding by using diagrams and animations for visual explanations. This allows the translation department to provide information in a way that is engaging and easy for children to understand. Furthermore, the translation department can automate the information translation and summarization process, providing up-to-date information in real time. For example, the translation department periodically scans the analyzed information and automatically translates and summarizes new information as it is added. The translation department also centrally manages the translation and summarization results, making them accessible to other departments. This allows the translation department to translate and summarize information efficiently and effectively, improving the overall system performance.

[0074] The Dialogue Department provides an interactive learning experience. For example, it provides immediate and appropriate answers when a child asks a question. Specifically, the Dialogue Department can provide instant answers to children's questions using generative AI. The generative AI analyzes the content of the question and generates an appropriate answer. For example, in response to a question about a science experiment, the Dialogue Department will explain the procedure and results of the experiment. Furthermore, the Dialogue Department can adjust the content and difficulty level of the answers according to the children's interests and level of understanding. This allows the Dialogue Department to provide information in a way that is interesting and easy for children to understand. In addition, the Dialogue Department can automate the dialogue process and provide up-to-date information in real time. For example, the Dialogue Department periodically scans the content of questions and automatically generates answers when new questions are added. The Dialogue Department also centrally manages the dialogue results and makes them accessible to other departments. This allows the Dialogue Department to provide information efficiently and effectively, improving the overall performance of the system.

[0075] The suggestion department analyzes learning history and proposes new topics. For example, the suggestion department uses a generative AI to analyze learning history and propose new topics based on individual interests. Specifically, the suggestion department can use generative AI to propose new information and topics based on children's interests. The generative AI analyzes learning history and identifies children's interests and concerns. For example, the suggestion department will propose new information and topics related to space to a child who is interested in space. The suggestion department can also evaluate learning progress and understanding based on children's learning history and propose appropriate topics. This allows the suggestion department to provide information in a way that is interesting to children and increases their motivation to learn. Furthermore, the suggestion department can automate the suggestion process and provide the latest information in real time. For example, the suggestion department can periodically scan learning history and automatically generate suggestions when new information is added. The suggestion department also centrally manages the suggestion results and makes them accessible to other departments. This allows the suggestion department to provide information efficiently and effectively and improve the overall performance of the system.

[0076] The data acquisition unit can retrieve information from highly reliable academic papers and public institution databases. For example, the data acquisition unit can retrieve information from highly reliable academic papers and public institution databases. The data acquisition unit can use generative AI to verify the reliability of the information and provide accurate information. For example, the data acquisition unit can extract necessary information from the latest scientific research and public institution reports. This allows for the provision of accurate knowledge by obtaining highly reliable information. Highly reliable academic papers and public institution databases include, but are not limited to, specific academic journals and government agency databases.

[0077] The analysis unit can verify the reliability of acquired information and provide accurate information. For example, the analysis unit can verify the reliability of acquired information and provide accurate information. The analysis unit can perform information analysis using generative AI. For example, the analysis unit can verify the reliability of information using evaluation criteria and verification methods for information sources. This allows for the provision of accurate information by verifying its reliability. Specific methods and criteria for verifying reliability include, but are not limited to, evaluation criteria and verification methods for information sources.

[0078] The translation department can translate and summarize complex technical terms in a way that is easy for children to understand. For example, the translation department can translate and summarize complex technical terms in a way that is easy for children to understand. Using generative AI, the translation department can replace specialized scientific terms with simpler language, making them easier for children to comprehend. For example, the translation department can facilitate understanding by using diagrams and animations for visual explanations. This facilitates children's understanding by translating and summarizing technical terms in an easy-to-understand manner. Specific examples of complex technical terms and translation methods include, but are not limited to, specific technical terms and translation standards.

[0079] The dialogue unit can provide immediate and appropriate answers when children ask questions. For example, the dialogue unit can provide immediate and appropriate answers when children ask questions. Using generative AI, the dialogue unit can provide immediate answers to children's questions. For example, in response to a question about a science experiment, the dialogue unit can explain the procedure and results of the experiment. This provides an environment where children can immediately resolve their questions. Specific criteria and methods for providing appropriate answers include, but are not limited to, accuracy and speed of the answer.

[0080] The suggestion function can analyze learning history and propose new topics based on individual interests. For example, the suggestion function can use a generative AI to analyze learning history and propose new topics based on individual interests. The suggestion function can use the generative AI to propose new information and topics based on children's interests. For example, the suggestion function will propose new information and topics related to space to a child who is interested in space. This continuously fosters a spirit of inquiry by proposing new topics based on individual interests. Specific methods and criteria for identifying individual interests include, but are not limited to, past learning history and survey results.

[0081] The information acquisition unit can estimate the user's emotions and adjust the timing of information acquisition based on the estimated emotions. For example, if the user is focused, the unit can acquire information immediately to avoid interrupting the learning flow. If the user is tired, the unit can temporarily delay information acquisition to encourage a break. If the user is excited, the unit can acquire information quickly to maintain interest. In this way, by adjusting the timing of information acquisition according to the user's emotions, the learning flow is not interrupted. Specific methods and criteria for estimating the user's emotions include, but are not limited to, facial recognition and voice analysis.

[0082] The data acquisition unit can analyze the user's past search history and select the optimal data acquisition method. For example, the data acquisition unit can prioritize acquiring information related to topics the user has frequently searched for in the past. The data acquisition unit can automatically acquire relevant information based on search keywords the user has used in the past. The data acquisition unit can acquire information related to a specific time period from the user's past search history. This allows the system to select the optimal information acquisition method by analyzing past search history. Specific criteria and selection methods for the optimal data acquisition method include, but are not limited to, search algorithms and filtering criteria.

[0083] The information retrieval unit can filter information based on the user's current learning status and areas of interest. For example, the unit can prioritize retrieving information related to the topic the user is currently studying. The unit can filter and retrieve highly relevant information based on the user's areas of interest. The unit can retrieve information of appropriate difficulty level according to the user's learning progress. This allows the system to provide highly relevant information by filtering it based on the user's current learning status and areas of interest. Specific criteria and methods for filtering include, but are not limited to, methods for identifying areas of interest and filtering algorithms.

[0084] The information retrieval unit can estimate the user's emotions and determine the priority of information to retrieve based on those emotions. For example, if the user is excited, the unit will prioritize retrieving highly relevant information to maintain their interest. If the user is tired, the unit will prioritize retrieving less burdensome information. If the user is focused, the unit will prioritize retrieving important information to avoid interrupting the learning flow. This enhances the effectiveness of learning by prioritizing information according to the user's emotions. Specific criteria and methods for determining information priority include, but are not limited to, importance evaluation criteria and prioritization algorithms.

[0085] The data acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location when acquiring information. For example, if the user is in a specific region, the data acquisition unit can prioritize the acquisition of information related to that region. If the user is traveling, the data acquisition unit can prioritize the acquisition of information related to the travel destination. If the user is participating in a specific event, the data acquisition unit can prioritize the acquisition of information related to that event. In this way, highly relevant information can be provided by considering geographical location. Specific methods and criteria for considering geographical location include, but are not limited to, the method of acquiring location information and the criteria for evaluating relevance.

[0086] The data acquisition unit can analyze a user's social media activity and acquire relevant information when acquiring data. For example, the data acquisition unit can prioritize acquiring information related to topics shared by the user on social media. The data acquisition unit can acquire relevant information based on the content of posts from accounts the user follows. The data acquisition unit can acquire information related to groups and events the user participates in on social media. This allows for the provision of highly relevant information by analyzing social media activity. Specific methods and criteria for analyzing social media activity include, but are not limited to, methods for analyzing post content and criteria for evaluating relevance.

[0087] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visual presentation. If the user is relaxed, the analysis unit can provide a presentation that includes detailed information. If the user is in a hurry, the analysis unit can provide a presentation that gets straight to the point. This facilitates understanding by adjusting the presentation of the analysis according to the user's emotions. Specific criteria and methods for adjusting the presentation of the analysis include, but are not limited to, visual presentation and textual conciseness.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit performs a detailed analysis of important information to promote a deeper understanding. For less important information, the analysis unit performs a concise analysis to efficiently provide the information. The analysis unit can adjust the level of detail of the analysis based on importance according to the user's learning objectives. This allows for efficient information delivery by adjusting the level of detail of the analysis based on the importance of the information. Specific criteria and methods for evaluating the importance of information include, but are not limited to, the reliability and relevance of the information.

[0089] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a scientific analysis algorithm to scientific information, a historical analysis algorithm to historical information, and an artistic analysis algorithm to artistic information. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the category of information. Specific types of analysis algorithms and their application methods include, but are not limited to, machine learning algorithms and statistical analysis algorithms.

[0090] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on those emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. If the user is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, appropriate information can be provided. Specific criteria and methods for adjusting the length of the analysis include, but are not limited to, the amount of information and the user's level of understanding.

[0091] The analysis unit can determine the priority of analysis based on when the information was acquired. For example, the analysis unit prioritizes the analysis of the latest information and provides it to the user. The analysis unit can analyze older information as needed. The analysis unit can determine the priority of analysis based on when the information was acquired, according to the user's learning objectives. This allows the system to provide the latest information by prioritizing analysis based on when the information was acquired. Specific methods and criteria for considering when the information was acquired include, but are not limited to, the priority of the latest information and the freshness of the information.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant information and provide it to the user. The analysis unit can postpone the analysis of less relevant information. The analysis unit can adjust the order of analysis based on the relevance of the information according to the user's learning objectives. This allows for efficient information delivery by adjusting the order of analysis based on the relevance of the information. Specific criteria and methods for evaluating the relevance of information include, but are not limited to, commonalities in information and relevance scoring.

[0093] The translation unit can estimate the user's emotions and adjust the translation's expression based on those emotions. For example, if the user is stressed, the unit can provide a simple and easy-to-read translation. If the user is relaxed, the unit can provide a translation that includes detailed information. If the user is in a hurry, the unit can provide a translation that gets straight to the point. This facilitates understanding by adjusting the translation's expression according to the user's emotions. Specific criteria and methods for adjusting the translation's expression include, but are not limited to, word choice and style adjustments.

[0094] The translation unit can adjust the level of detail in translations based on the importance of technical terms. For example, it can provide detailed translations of important technical terms to promote deeper understanding, and concise translations of less important technical terms to efficiently deliver information. The translation unit can also adjust the level of detail in translations based on the importance of technical terms according to the user's learning objectives. This allows for efficient information delivery by adjusting the level of detail in translations based on the importance of technical terms. Specific criteria and methods for evaluating the importance of technical terms include, but are not limited to, the frequency of the term and the difficulty of understanding it.

[0095] The translation unit can apply different translation algorithms depending on the category of information during translation. For example, it can apply a scientific translation algorithm to scientific information, a historical translation algorithm to historical information, and an artistic translation algorithm to artistic information. This improves the accuracy of translations by applying the appropriate translation algorithm according to the category of information. Specific types and application methods of translation algorithms include, but are not limited to, machine translation algorithms and rule-based translation.

[0096] The translation unit can estimate the user's emotions and adjust the translation length based on that estimation. For example, if the user is in a hurry, the unit can provide a short, concise translation. If the user is relaxed, the unit can provide a detailed translation. If the user is excited, the unit can provide a visually stimulating translation. By adjusting the translation length according to the user's emotions, the unit can provide appropriate information. Specific criteria and methods for adjusting translation length include, but are not limited to, sentence length and the degree of information condensation.

[0097] The translation department can prioritize translations based on when the information was acquired. For example, the department prioritizes translating the latest information and providing it to the user. The department can translate older information as needed. The department can prioritize translations based on when the information was acquired, according to the user's learning objectives. This allows the department to provide the latest information by prioritizing translations based on when the information was acquired. Specific methods and criteria for considering when the information was acquired include, but are not limited to, the priority of the latest information and the freshness of the information.

[0098] The translation unit can adjust the order of translations based on the relevance of the information during the translation process. For example, the translation unit can prioritize translating highly relevant information and provide it to the user. The translation unit can postpone the translation of less relevant information. The translation unit can adjust the order of translations based on the relevance of the information according to the user's learning objectives. This allows for efficient information delivery by adjusting the order of translations based on the relevance of the information. Specific criteria and methods for evaluating the relevance of information include, but are not limited to, commonalities in information and relevance scoring.

[0099] The dialogue unit can estimate the user's emotions and adjust the way the dialogue is presented based on those emotions. For example, if the user is nervous, the dialogue unit can provide a simple and easy-to-understand dialogue. If the user is relaxed, the dialogue unit can provide a dialogue that includes detailed information. If the user is in a hurry, the dialogue unit can provide a dialogue that gets straight to the point. This facilitates understanding by adjusting the way the dialogue is presented according to the user's emotions. Specific criteria and methods for adjusting the way the dialogue is presented include, but are not limited to, word choice and style adjustments.

[0100] The dialogue unit can adjust the level of detail in its answers based on the importance of the questions during the conversation. For example, it can provide detailed answers to important questions to promote a deeper understanding. For less important questions, it can provide concise answers to efficiently deliver information. The dialogue unit can adjust the level of detail in its answers based on the importance of the questions according to the user's learning objectives. This allows for efficient information delivery by adjusting the level of detail in answers based on the importance of the questions. Specific criteria and methods for evaluating the importance of questions include, but are not limited to, the frequency and relevance of the questions.

[0101] The dialogue unit can apply different dialogue algorithms depending on the category of the question during a conversation. For example, it can apply a scientific dialogue algorithm to questions about science, a historical dialogue algorithm to questions about history, and an artistic dialogue algorithm to questions about art. This improves the accuracy of the conversation by applying the appropriate dialogue algorithm according to the category of the question. Specific types and application methods of dialogue algorithms include, but are not limited to, natural language processing algorithms and rule-based dialogues.

[0102] The dialogue unit can estimate the user's emotions and adjust the length of the dialogue based on those emotions. For example, if the user is in a hurry, the dialogue unit can provide a short, to-the-point dialogue. If the user is relaxed, the dialogue unit can provide a detailed dialogue. If the user is excited, the dialogue unit can provide a visually stimulating dialogue. By adjusting the length of the dialogue according to the user's emotions, the system can provide appropriate information. Specific criteria and methods for adjusting the length of the dialogue include, but are not limited to, the number of dialogues and the degree of information condensation.

[0103] The dialogue unit can prioritize answers based on when the questions were submitted during the dialogue. For example, the dialogue unit will prioritize answering the most recent questions and provide them to the user. The dialogue unit can answer older questions as needed. The dialogue unit can prioritize answers based on when the questions were submitted, according to the user's learning objectives. This allows the system to provide up-to-date information by prioritizing answers based on when the questions were submitted. Specific methods and criteria for considering when questions were submitted include, but are not limited to, prioritizing the most recent questions and the freshness of the questions.

[0104] The dialogue unit can adjust the order of answers based on the relevance of the questions during the conversation. For example, the dialogue unit can prioritize answering and providing answers to highly relevant questions to the user. The dialogue unit can postpone answering less relevant questions. The dialogue unit can adjust the order of answers based on the relevance of the questions according to the user's learning objectives. This allows for efficient information delivery by adjusting the order of answers based on the relevance of the questions. Specific criteria and methods for evaluating the relevance of questions include, but are not limited to, commonalities between questions and relevance scoring.

[0105] The suggestion function can estimate the user's emotions and adjust the way the suggestion is presented based on those emotions. For example, if the user is stressed, the suggestion function can provide a simple and easily understandable suggestion. If the user is relaxed, the suggestion function can provide a suggestion that includes detailed information. If the user is in a hurry, the suggestion function can provide a suggestion that gets straight to the point. This facilitates understanding by adjusting the way the suggestion is presented according to the user's emotions. Specific criteria and methods for adjusting the way the suggestion is presented include, but are not limited to, word choice and style adjustments.

[0106] The suggestion function can adjust the level of detail of suggestions based on the importance of the learning history. For example, for important learning history, the suggestion function can provide detailed suggestions to promote a deeper understanding. For less important learning history, the suggestion function can provide concise suggestions to efficiently deliver information. The suggestion function can adjust the level of detail of suggestions based on the importance of the learning history according to the user's learning objectives. This allows for efficient information delivery by adjusting the level of detail of suggestions based on the importance of the learning history. Specific criteria and methods for evaluating the importance of learning history include, but are not limited to, the frequency and relevance of learning.

[0107] The suggestion function can apply different suggestion algorithms depending on the category of the learning history during the suggestion process. For example, the suggestion function can apply a scientific suggestion algorithm to a learning history related to science, a historical suggestion algorithm to a learning history related to history, and an artistic suggestion algorithm to a learning history related to art. This improves the accuracy of the suggestions by applying the appropriate suggestion algorithm according to the category of the learning history. Specific types of suggestion algorithms and their application methods include, but are not limited to, recommendation algorithms and machine learning algorithms.

[0108] The suggestion section can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the suggestion section can provide short, concise suggestions. If the user is relaxed, the suggestion section can provide detailed suggestions. If the user is excited, the suggestion section can provide visually stimulating suggestions. By adjusting the length of suggestions according to the user's emotions, the system can provide appropriate information. Specific criteria and methods for adjusting suggestion length include, but are not limited to, the number of suggestions and the degree of information condensation.

[0109] The suggestion team can prioritize suggestions based on the timing of learning history submissions. For example, the suggestion team may prioritize suggesting and providing the most recent learning history to the user. The suggestion team can also suggest older learning history as needed. The suggestion team can prioritize suggestions based on the timing of learning history submissions according to the user's learning objectives. This allows for the provision of up-to-date information by prioritizing suggestions based on the timing of learning history submissions. Specific methods and criteria for considering the timing of learning history submissions include, but are not limited to, the priority given to the most recent learning history and the freshness of the history.

[0110] The suggestion function can adjust the order of suggestions based on the relevance of the learning history. For example, the suggestion function can prioritize suggesting and providing highly relevant learning history to the user. The suggestion function can postpone suggesting less relevant learning history. The suggestion function can adjust the order of suggestions based on the relevance of the learning history according to the user's learning objectives. This allows for efficient information delivery by adjusting the order of suggestions based on the relevance of the learning history. Specific criteria and methods for evaluating the relevance of learning history include, but are not limited to, commonalities in history and relevance scoring.

[0111] The suggestion function can propose new topics based on the user's learning history. For example, the suggestion function can suggest new information related to topics the user has previously studied. The suggestion function can suggest new topics that the user might be interested in based on their learning history. The suggestion function can analyze the user's learning history and suggest topics to learn next. In this way, by suggesting new topics based on the user's learning history, a sense of curiosity is continuously fostered. Specific criteria and methods for suggesting new topics include, but are not limited to, topic selection criteria and suggestion methods.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The information acquisition unit can monitor the user's learning progress in real time and adjust the frequency of information acquisition according to that progress. For example, if the user is progressing smoothly, the frequency of information acquisition can be increased to maintain the learning flow. Conversely, if the user is struggling with learning, the frequency of information acquisition can be reduced to allow time for deeper understanding. The information acquisition unit can also adjust the difficulty level of the next information acquired based on the user's learning progress. This makes it possible to provide information that matches the user's learning pace, thereby improving learning efficiency.

[0114] The analysis unit can analyze the user's learning history and evaluate the reliability of information based on past learning. For example, if a user has frequently used highly reliable information sources in the past, it will prioritize analyzing information from those sources. Conversely, if a user has frequently used unreliable information sources, it will carefully analyze information from those sources. The analysis unit can also re-evaluate the reliability of specific information sources based on the user's learning history and adjust the analysis algorithm as needed. This enables reliability evaluation based on the user's learning history, resulting in the provision of more accurate information.

[0115] The translation department can customize the translation method according to the user's learning style. For example, users who prefer visual learning can be provided with translations that make extensive use of diagrams and animations. Users who prefer auditory learning can have audio explanations added. Furthermore, users who prefer tactile learning can be provided with translations that incorporate interactive elements. This allows for translations tailored to the user's learning style, leading to a deeper understanding.

[0116] The dialogue unit can provide answers based on the user's learning history, referencing previously asked questions. For example, if a user has asked the same question before, it can reuse that answer to provide a quick response. It can also provide additional relevant information based on past questions. This enables efficient dialogue utilizing the user's learning history, thereby improving the quality of learning.

[0117] The suggestion function can analyze the user's learning history and suggest new topics according to their learning progress. For example, if a user has a solid understanding of a particular topic, it will suggest more advanced content related to that topic. Conversely, if their understanding is insufficient, it will suggest basic content again. The suggestion function can also visualize the user's learning progress based on their learning history and visually present the next topic they should learn. This enables appropriate suggestions tailored to the user's learning progress and helps maintain their motivation to learn.

[0118] The information acquisition unit can estimate the user's emotions and adjust the method of information acquisition based on the estimated emotions. For example, if the user is excited, it quickly acquires highly relevant information to maintain their interest. If the user is tired, it acquires less burdensome information and encourages them to take a break. Also, if the user is focused, it can acquire information immediately to avoid interrupting the learning flow. This makes it possible to acquire information in accordance with the user's emotions, thereby improving the effectiveness of learning.

[0119] The analysis unit can estimate the user's emotions and adjust the analysis method based on those emotions. For example, if the user is nervous, it can provide a simple and easy-to-understand analysis. If the user is relaxed, it can provide an analysis that includes detailed information. If the user is in a hurry, it can provide a concise analysis. This enables analysis tailored to the user's emotions, thereby facilitating understanding.

[0120] The translation unit can estimate the user's emotions and adjust the translation method based on those estimates. For example, if the user is stressed, it can provide a simple and easy-to-read translation. If the user is relaxed, it can provide a translation that includes detailed information. And if the user is in a hurry, it can provide a translation that gets straight to the point. This enables translations that are tailored to the user's emotions, thereby facilitating understanding.

[0121] The dialogue unit can estimate the user's emotions and adjust the dialogue method based on those estimates. For example, if the user is nervous, it can provide a simple and easy-to-understand dialogue. If the user is relaxed, it can provide a dialogue that includes detailed information. If the user is in a hurry, it can provide a concise dialogue. This enables dialogue that is tailored to the user's emotions, thereby promoting understanding.

[0122] The suggestion function can estimate the user's emotions and adjust the suggestion method based on those emotions. For example, if the user is stressed, it can provide simple and highly visible suggestions. If the user is relaxed, it can provide suggestions that include detailed information. If the user is in a hurry, it can provide suggestions that get straight to the point. This enables suggestions that are tailored to the user's emotions, thereby facilitating understanding.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The acquisition unit acquires highly reliable information. For example, it acquires information from reliable academic papers and public institution databases, verifies the reliability of the information using generation AI, and provides accurate information. Step 2: The analysis unit analyzes the information acquired by the acquisition unit. For example, it verifies the reliability of the acquired information and performs analysis of the information using a generation AI. Step 3: The translation unit translates and summarizes the information analyzed by the analysis unit for children. For example, it translates and summarizes complex technical terms in a way that is easy for children to understand, uses generative AI to replace specialized scientific terms with simpler language, and explains them visually using diagrams and animations. Step 4: The dialogue section provides an interactive learning experience. For example, it provides an immediate and appropriate answer when a child asks a question, using generative AI to instantly answer children's questions. Step 5: The suggestion department analyzes the learning history and proposes new topics. For example, a generating AI analyzes the learning history and proposes new topics based on individual interests.

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0128] Each of the multiple elements described above, including the acquisition unit, analysis unit, translation unit, dialogue unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the smart device 14 and acquires information from reliable academic papers and public institution databases. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired information. The translation unit is implemented by the control unit 46A of the smart device 14 and translates and summarizes the analyzed information for children. The dialogue unit is implemented by the control unit 46A of the smart device 14 and provides an interactive learning experience. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the learning history to propose new topics. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the acquisition unit, analysis unit, translation unit, dialogue unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the smart glasses 214 and acquires information from reliable academic papers and public institution databases. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired information. The translation unit is implemented by the control unit 46A of the smart glasses 214 and translates and summarizes the analyzed information for children. The dialogue unit is implemented by the control unit 46A of the smart glasses 214 and provides an interactive learning experience. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the learning history to suggest new topics. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the acquisition unit, analysis unit, translation unit, dialogue unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the headset terminal 314 and acquires information from reliable academic papers and public institution databases. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired information. The translation unit is implemented by the control unit 46A of the headset terminal 314 and translates and summarizes the analyzed information for children. The dialogue unit is implemented by the control unit 46A of the headset terminal 314 and provides an interactive learning experience. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the learning history to propose new topics. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the acquisition unit, analysis unit, translation unit, dialogue unit, and proposal unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the robot 414 and acquires information from reliable academic papers and public institution databases. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired information. The translation unit is implemented by the control unit 46A of the robot 414 and translates and summarizes the analyzed information for children. The dialogue unit is implemented by the control unit 46A of the robot 414 and provides an interactive learning experience. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the learning history to propose new topics. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0178] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] 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.

[0188] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) An acquisition unit that acquires highly reliable information, An analysis unit analyzes the information acquired by the acquisition unit, The translation unit translates and summarizes the information analyzed by the aforementioned analysis unit for use by children, The Dialogue Department provides an interactive learning experience, It includes a suggestion section that analyzes learning history and proposes new topics. A system characterized by the following features. (Note 2) The acquisition unit is, Information is obtained from highly reliable academic papers and public institution databases. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We verify the reliability of the information obtained and provide accurate information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned translation department, Translate and summarize complex technical terms in a way that is easy for children to understand. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned dialogue unit, Provide immediate and appropriate answers when children ask questions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We analyze learning history and suggest new topics based on individual interests. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, It estimates the user's emotions and adjusts the timing of information acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze the user's past search history and select the optimal method for retrieving it. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When retrieving information, filtering is performed based on the user's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When retrieving information, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned translation department, It estimates the user's emotions and adjusts the translation's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned translation department, During translation, adjust the level of detail based on the importance of technical terms. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned translation department, During translation, different translation algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned translation department, It estimates the user's sentiment and adjusts the translation length based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned translation department, During translation, prioritize translations based on when the information was obtained. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned translation department, During translation, the order of translations is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way the dialogue is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dialogue unit, During the conversation, adjust the level of detail in the answer based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned dialogue unit, During the conversation, different dialogue algorithms are applied depending on the category of the question. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the length of the conversation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned dialogue unit, During the dialogue, we will prioritize the answers based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned dialogue unit, During the conversation, adjust the order of answers based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the learning history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the category of the learning history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When submitting a proposal, the priority of the proposal will be determined based on when the learning history was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the learning history. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal section is, When making suggestions, new topics are proposed based on the user's learning history. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires highly reliable information, An analysis unit analyzes the information acquired by the acquisition unit, The translation unit translates and summarizes the information analyzed by the aforementioned analysis unit for use by children, The Dialogue Department provides an interactive learning experience, It includes a suggestion section that analyzes learning history and proposes new topics. A system characterized by the following features.

2. The acquisition unit is, Information is obtained from highly reliable academic papers and public institution databases. The system according to feature 1.

3. The aforementioned analysis unit, We verify the reliability of the information obtained and provide accurate information. The system according to feature 1.

4. The aforementioned translation department, Translate and summarize complex technical terms in a way that is easy for children to understand. The system according to feature 1.

5. The aforementioned dialogue unit, Provide immediate and appropriate answers when children ask questions. The system according to feature 1.

6. The aforementioned proposal section is, We analyze learning history and suggest new topics based on individual interests. The system according to feature 1.

7. The acquisition unit is, It estimates the user's emotions and adjusts the timing of information acquisition based on the estimated user emotions. The system according to feature 1.

8. The acquisition unit is, Analyze the user's past search history and select the optimal method for retrieving it. The system according to feature 1.

9. The acquisition unit is, When retrieving information, filtering is performed based on the user's current learning status and areas of interest. The system according to feature 1.

10. The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system according to feature 1.

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