system

Abstract

The system according to this embodiment aims to make news articles easier for children to understand and to help them learn with interest. [Solution] The system according to this embodiment comprises a selection unit, a summarization unit, and a learning support unit. The selection unit selects news articles based on the child's interests and age. The summarization unit summarizes the news articles selected by the selection unit. The learning support unit provides quizzes and games based on the news articles summarized by the summarization unit.

Description

Technical Field

[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 the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for children to understand news articles and learn with interest.

[0005] The system according to the embodiment aims to enable children to easily understand news articles and learn with interest.

Means for Solving the Problems

[0006] The system according to the embodiment includes a selection unit, a summarization unit, and a learning support unit. The selection unit selects news articles based on children's interests and ages. The summarization unit summarizes the news articles selected by the selection unit. The learning support unit provides quizzes and games based on the news articles summarized by the summarization unit.

Effects of the Invention

[0007] The system according to this embodiment makes it easier for children to understand news articles and learn with interest. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 children's news summarization platform according to an embodiment of the present invention is a system that automatically selects news articles tailored to a child's interests and age, and summarizes them in an easy-to-understand manner. The children's news summarization platform uses AI to automatically select and display the most interesting and informative news articles according to a child's interests and age. For example, it displays news articles about animals for a child interested in animals, and news articles about science for a child interested in science. In this way, it can provide articles that are likely to interest children. Next, as a news summarization function, it shortens long articles to make them easy to understand and displays only the important points. For example, the AI ​​analyzes the news article, extracts important information, and summarizes it so that even children can easily understand it. This makes even difficult news articles easier for children to understand. Furthermore, it also provides functions to support learning through quizzes and games. For example, it presents quizzes related to news articles to check whether children understand the content of the news. In addition, children can learn about the news content in a fun way through games. This can increase children's motivation to learn. This mechanism makes it possible for children to learn about the news in an enjoyable way, and an improvement in news literacy can be expected. Also, even if it is difficult for children to choose news themselves, the AI ​​will automatically select it for them, making it easier for them to select and filter information. For example, by having AI select and summarize news articles based on a child's interests and age, it becomes easier for children to understand the news. Furthermore, children can learn about the news in a fun way through quizzes and games. This increases children's motivation to learn and improves their news literacy. Thus, an AI-powered news summarization platform for children provides an environment where children can learn about the news in an enjoyable way, increasing their motivation to learn and improving their news literacy. In short, a news summarization platform for children allows children to learn about the news in an enjoyable way by selecting and summarizing news articles based on their interests and age, and by offering quizzes and games.

[0029] The children's news summarization platform according to this embodiment comprises a selection unit, a summarization unit, and a learning support unit. The selection unit selects news articles based on the child's interests and age. The selection unit can, for example, identify the child's interests from surveys or past behavioral data. The selection unit can also classify news articles by age group of children and select appropriate articles. For example, the selection unit displays news articles about animals to children interested in animals, and news articles about science to children interested in science. The summarization unit analyzes news articles, extracts important information, and summarizes it. The summarization unit can, for example, use AI to analyze news articles, extract important points, and summarize them. The summarization unit performs summarization based on the length of the text and the importance of the information being summarized. For example, the summarization unit can shorten long news articles to make them easy to understand and display only the important points. The learning support unit provides quizzes and games related to the news articles. The learning support unit can, for example, present quizzes related to news articles to check whether the child understands the news content. The learning support unit can also make learning about the news content fun through games. For example, the learning support section provides puzzle games related to news articles, allowing children to learn about the news in a fun way. Thus, the children's news summarization platform according to this embodiment selects and summarizes news articles based on the child's interests and age, and provides quizzes and games, enabling children to learn about the news in an enjoyable way.

[0030] The selection function chooses news articles based on the child's interests and age. For example, the selection function can identify a child's interests through surveys or past behavioral data. Specifically, surveys are conducted in the form of online forms or in-app questions, allowing children to select themes and topics that interest them. Past behavioral data is collected by analyzing the child's history of articles read, videos watched, and events attended. This allows the selection function to accurately understand the child's interests and select appropriate news articles. The selection function can also categorize news articles by the child's age group and select appropriate articles. For example, it can provide articles written in simple language for toddlers, slightly more difficult articles for elementary school students, and articles with more detailed information for middle school students. To perform these classifications, the selection function can use algorithms that automatically evaluate the content and difficulty level of articles. Furthermore, the selection function can customize the display order and layout of articles according to the child's interests and age. For example, news articles about animals can be displayed first for children interested in animals, and news articles about science can be prioritized for children interested in science. This allows the selection function to effectively provide articles that are likely to interest children and increase their engagement with the news.

[0031] The summarization function analyzes news articles, extracts key information, and summarizes it. For example, it can use AI to analyze news articles, extract key points, and summarize them. Specifically, it uses natural language processing technology to analyze the article's content and identify keywords and important phrases. The AI ​​learns a model to understand the context and structure of the article and extract key information. For example, it can summarize a long news article into a short and easy-to-understand format, displaying only the essential points. The summarization function performs summaries based on the length of the text and the importance of the information being summarized. For example, it might focus on the beginning or conclusion of the article, omitting detailed explanations and background information. Furthermore, the summarization function can adjust the difficulty and level of detail of the summary according to the child's age and comprehension level. For example, it can provide a short summary in simple language for preschoolers and a summary with slightly more detailed information for elementary school children. In addition, the summarization function can use illustrations and icons to visually present the summarized information in an easy-to-understand way. This allows the summarization function to help children easily understand the key points of news articles and increase their interest in the news.

[0032] The Learning Support Department provides quizzes and games related to news articles. For example, it can create quizzes related to news articles to check whether children understand the content. Specifically, it provides multiple-choice quizzes based on the content of news articles and fill-in-the-blank quizzes based on the content of the articles. This allows children to review the content of news articles after reading them and check their understanding. The Learning Support Department can also make learning news content fun through games. For example, it provides puzzle games related to news articles and storytelling games based on the content of the articles. This allows children to learn news content in an enjoyable way and solidify it in their memory. Furthermore, the Learning Support Department can track children's learning progress and provide appropriate feedback. For example, it records the correct answer rate for quizzes and the completion status of games to evaluate the child's understanding and learning progress. Based on this, it can suggest articles to read next or quizzes to try. In this way, the Learning Support Department allows children to learn news in an enjoyable way and deepen their understanding effectively.

[0033] The selection unit can analyze a child's past browsing history and select the most suitable news articles. For example, the selection unit can prioritize articles from categories that the child has frequently viewed in the past. It can also analyze the trends of articles that the child has previously given high ratings to and select similar articles. Furthermore, the selection unit can extract the characteristics of articles that the child has viewed for extended periods in the past and select articles with similar characteristics. In this way, by analyzing past browsing history, the system can provide the most suitable news articles for the child. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the child's browsing history data into a generating AI and have the generating AI perform the selection of the most suitable news articles.

[0034] The selection unit can filter news articles based on the child's current learning progress and areas of interest. For example, the selection unit can select articles at an easily understandable level according to the child's learning progress. It can also prioritize relevant articles based on the child's areas of interest. Furthermore, the selection unit can select articles that will help the child achieve their learning goals. In this way, by filtering news articles based on learning progress and areas of interest, appropriate news articles can be provided to the child. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the child's learning progress data into a generating AI and have the generating AI perform the filtering of news articles.

[0035] The selection unit can prioritize selecting news articles that are highly relevant to the child, taking into account the child's geographical location. For example, the selection unit can prioritize news articles related to the child's area of ​​residence. Furthermore, if the child is traveling, the selection unit can prioritize news articles related to their travel destination. Additionally, the selection unit can prioritize news articles related to the child's school or local events. This allows the system to provide news articles that are highly relevant to the child by considering their geographical location. Some or all of the above processing in the selection unit may be performed using AI, or without AI. For example, the selection unit can input the child's geographical location data into a generating AI and have the generating AI select highly relevant news articles.

[0036] The selection unit can analyze a child's social media activity and select relevant articles when choosing news articles. For example, the selection unit can select articles related to topics that the child frequently shares on social media. It can also select articles based on the content of posts from accounts that the child follows on social media. Furthermore, the selection unit can select articles related to the areas of interest of groups that the child participates in on social media. In this way, by analyzing social media activity, it is possible to provide news articles that are highly relevant to the child. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the child's social media data into a generating AI and have the generating AI perform the selection of relevant news articles.

[0037] The summarization unit can adjust the level of detail in the summary based on the importance of the news article during summary generation. For example, the summarization unit can provide a detailed summary for news articles of high importance, and a concise summary for news articles of low importance. Furthermore, the summarization unit can dynamically adjust the length and level of detail of the summary according to its importance. This allows for the provision of appropriate summaries by adjusting the level of detail based on the importance of the news article. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input news article importance data into a generation AI and have the generation AI perform the calculation of the level of detail in the summary.

[0038] The summarization unit can apply different summarization algorithms depending on the category of the news article when generating summaries. For example, the summarization unit can apply a summarization algorithm that simplifies technical terms to science articles. It can also apply a summarization algorithm that emphasizes match results to sports articles. Furthermore, it can apply a summarization algorithm that emphasizes storytelling to entertainment articles. By applying a summarization algorithm according to the category of the news article, it is possible to provide an appropriate summary. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input news article category data into a generating AI and have the generating AI perform the application of the summarization algorithm.

[0039] The summarization unit can determine the priority of summaries based on the publication date of news articles when generating summaries. For example, the summarization unit prioritizes summarizing the most recent news articles. The summarization unit can also adjust the priority of summaries for past news articles according to their importance. Furthermore, the summarization unit can dynamically adjust the order of summaries based on their publication date. This allows for the provision of appropriate summaries by determining the priority of summaries based on the publication date of news articles. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input news article publication date data into a generation AI and have the generation AI determine the priority of summaries.

[0040] The summarization unit can adjust the order of summaries based on the relevance of news articles during summary generation. For example, the summarization unit prioritizes summarizing highly relevant news articles. It can also summarize less relevant news articles concisely. Furthermore, the summarization unit can dynamically adjust the order of summaries based on relevance. This allows for the provision of appropriate summaries by adjusting the order of summaries based on the relevance of news articles. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input news article relevance data into a generation AI and have the generation AI execute the order of summaries.

[0041] The learning support unit can select the most suitable questions for quizzes and games by referring to the child's past learning history. For example, the learning support unit can re-present questions that the child previously answered incorrectly. It can also prioritize questions in areas where the child has previously excelled. Furthermore, the learning support unit can select a balanced set of questions based on the child's learning history. In this way, by referring to past learning history, it can provide the child with the most suitable questions. Some or all of the above processing in the learning support unit may be performed using AI, for example, or not. For example, the learning support unit can input the child's learning history data into a generating AI and have the generating AI select the most suitable questions.

[0042] The learning support unit can apply different learning methods based on the content of news articles when creating quizzes and games. For example, in a quiz based on a science article, the learning support unit can ask questions about experimental results. In a quiz based on a history article, the learning support unit can also ask questions about dates and events. Furthermore, in a quiz based on an entertainment article, the learning support unit can ask questions about story comprehension. By applying different learning methods based on the content of news articles, more effective learning can be provided. Some or all of the above processing in the learning support unit may be performed using AI, for example, or without AI. For example, the learning support unit can input news article content data into a generating AI and have the generating AI execute the application of different learning methods.

[0043] The learning support unit can prioritize presenting questions that are highly relevant to the child, taking into account the child's geographical location when presenting quizzes and games. For example, the learning support unit can prioritize questions related to the child's residential area. Furthermore, if the child is traveling, the learning support unit can prioritize questions related to their travel destination. It can also prioritize questions related to the child's school or local events. This allows the learning support unit to provide questions that are highly relevant to the child by considering their geographical location. Some or all of the above processing in the learning support unit may be performed using AI, or not. For example, the learning support unit can input the child's geographical location data into a generating AI and have the generating AI select highly relevant questions.

[0044] The learning support unit can improve the accuracy of quizzes and games by referring to related literature in news articles. For example, the learning support unit can create questions by referring to academic papers related to news articles. It can also create questions by referring to books related to news articles. Furthermore, the learning support unit can create questions by referring to websites related to news articles. In this way, the accuracy of the questions can be improved by referring to related literature. Some or all of the above processing in the learning support unit may be performed using AI, for example, or without AI. For example, the learning support unit can input related literature data for news articles into a generating AI and have the generating AI perform the task of improving the accuracy of the questions.

[0045] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0046] The children's news summary platform may also include a text-to-speech function. This function provides the ability to read news articles aloud. For example, it can provide news articles in audio format to children who have difficulty reading or who are visually impaired. The text-to-speech function can also read the news articles at a slower pace to make them easier for children to understand. Furthermore, the text-to-speech function can highlight important points in the news articles, making them easier for children to comprehend. Some or all of the above processing in the text-to-speech function may be performed using AI, or not. For example, the text-to-speech function can input the text data of the news article into a generating AI and have the generating AI perform the text-to-speech.

[0047] The news summarization platform for children may also include an interactive question-and-answer section. This section provides a function that allows children to interactively answer questions related to news articles. For example, after reading a news article, questions about its content can be posed to the child, and their understanding can be checked based on their answers. The interactive question-and-answer section can also provide additional information and explanations based on the child's answers. Furthermore, the interactive question-and-answer section can provide a mechanism where children earn points for answering questions, thereby increasing their motivation to learn. This allows for a deeper understanding of the news article's content and promotes learning. Some or all of the above processing in the interactive question-and-answer section may be performed using AI, for example, or without AI. For example, the interactive question-and-answer section could input news article content data into a generating AI and have the generating AI generate the questions and answers.

[0048] A news summary platform for children can also include a function to automatically display relevant videos and images based on the content of news articles. For example, it can automatically search for videos related to news articles and display them next to the article. It can also automatically display images related to news articles. Furthermore, it can display relevant infographics and charts based on the content of news articles. This visually complements the content of news articles, making them easier for children to understand. Some or all of the above processes for displaying relevant videos and images may be performed using AI, for example, or not. For example, for displaying relevant videos and images, news article content data could be input into a generating AI, and the generating AI could be made to search for and display relevant media.

[0049] A news summary platform for children can also include a function to recommend related books and materials based on the content of news articles. For example, it can automatically search for books related to news articles and recommend them to children. It can also recommend learning materials and websites related to news articles. Furthermore, it can recommend related documentaries and movies based on the content of news articles. This can provide resources for deeper learning about the content of news articles and support children's learning. Some or all of the above processes in recommending related books and materials may be performed using AI, for example, or not using AI. For example, for recommending related books and materials, news article content data can be input into a generating AI, and the generating AI can be made to search for and display recommended resources.

[0050] A news summary platform for children can also include a function to suggest relevant experiments and activities based on the content of news articles. For example, it could suggest experiments related to science articles, allowing children to deepen their learning by actually performing the experiments. It could also suggest activities related to history articles, allowing children to experience historical events. Furthermore, it could suggest activities related to entertainment articles, allowing children to learn while having fun. This allows children to learn the content of news articles through real-world experiences, thereby increasing their motivation to learn. Some or all of the above-mentioned processes in suggesting relevant experiments and activities may be performed using AI, for example, or not. For example, the suggestion of relevant experiments and activities could be done by inputting the content data of the news article into a generating AI, and having the generating AI perform the generation of the suggested content.

[0051] The following briefly describes the processing flow for example form 1.

[0052] Step 1: The selection function chooses news articles based on the child's interests and age. The selection function can identify a child's interests, for example, through surveys or past behavioral data. It can also categorize news articles by age group and select appropriate articles. For example, it can display news articles about animals to children interested in animals, and news articles about science to children interested in science. Step 2: The summarization unit analyzes the news article, extracts important information, and summarizes it. The summarization unit can, for example, use AI to analyze the news article, extract the key points, and summarize them. The summarization unit performs summarization based on the length of the text and the importance of the information being summarized. For example, it can summarize a long news article into a short and easy-to-understand format, displaying only the important points. Step 3: The learning support department provides quizzes and games related to news articles. For example, the learning support department can create quizzes related to news articles to check whether children understand the content of the news. The learning support department can also make learning about the news fun through games. For example, it can provide puzzle games related to news articles so that children can learn about the news in an enjoyable way.

[0053] (Example of form 2) The children's news summarization platform according to an embodiment of the present invention is a system that automatically selects news articles tailored to a child's interests and age, and summarizes them in an easy-to-understand manner. The children's news summarization platform uses AI to automatically select and display the most interesting and informative news articles according to a child's interests and age. For example, it displays news articles about animals for a child interested in animals, and news articles about science for a child interested in science. In this way, it can provide articles that are likely to interest children. Next, as a news summarization function, it shortens long articles to make them easy to understand and displays only the important points. For example, the AI ​​analyzes the news article, extracts important information, and summarizes it so that even children can easily understand it. This makes even difficult news articles easier for children to understand. Furthermore, it also provides functions to support learning through quizzes and games. For example, it presents quizzes related to news articles to check whether children understand the content of the news. In addition, children can learn about the news content in a fun way through games. This can increase children's motivation to learn. This mechanism makes it possible for children to learn about the news in an enjoyable way, and an improvement in news literacy can be expected. Also, even if it is difficult for children to choose news themselves, the AI ​​will automatically select it for them, making it easier for them to select and filter information. For example, by having AI select and summarize news articles based on a child's interests and age, it becomes easier for children to understand the news. Furthermore, children can learn about the news in a fun way through quizzes and games. This increases children's motivation to learn and improves their news literacy. Thus, an AI-powered news summarization platform for children provides an environment where children can learn about the news in an enjoyable way, increasing their motivation to learn and improving their news literacy. In short, a news summarization platform for children allows children to learn about the news in an enjoyable way by selecting and summarizing news articles based on their interests and age, and by offering quizzes and games.

[0054] The children's news summarization platform according to this embodiment comprises a selection unit, a summarization unit, and a learning support unit. The selection unit selects news articles based on the child's interests and age. The selection unit can, for example, identify the child's interests from surveys or past behavioral data. The selection unit can also classify news articles by age group of children and select appropriate articles. For example, the selection unit displays news articles about animals to children interested in animals, and news articles about science to children interested in science. The summarization unit analyzes news articles, extracts important information, and summarizes it. The summarization unit can, for example, use AI to analyze news articles, extract important points, and summarize them. The summarization unit performs summarization based on the length of the text and the importance of the information being summarized. For example, the summarization unit can shorten long news articles to make them easy to understand and display only the important points. The learning support unit provides quizzes and games related to the news articles. The learning support unit can, for example, present quizzes related to news articles to check whether the child understands the news content. The learning support unit can also make learning about the news content fun through games. For example, the learning support section provides puzzle games related to news articles, allowing children to learn about the news in a fun way. Thus, the children's news summarization platform according to this embodiment selects and summarizes news articles based on the child's interests and age, and provides quizzes and games, enabling children to learn about the news in an enjoyable way.

[0055] The selection function chooses news articles based on the child's interests and age. For example, the selection function can identify a child's interests through surveys or past behavioral data. Specifically, surveys are conducted in the form of online forms or in-app questions, allowing children to select themes and topics that interest them. Past behavioral data is collected by analyzing the child's history of articles read, videos watched, and events attended. This allows the selection function to accurately understand the child's interests and select appropriate news articles. The selection function can also categorize news articles by the child's age group and select appropriate articles. For example, it can provide articles written in simple language for toddlers, slightly more difficult articles for elementary school students, and articles with more detailed information for middle school students. To perform these classifications, the selection function can use algorithms that automatically evaluate the content and difficulty level of articles. Furthermore, the selection function can customize the display order and layout of articles according to the child's interests and age. For example, news articles about animals can be displayed first for children interested in animals, and news articles about science can be prioritized for children interested in science. This allows the selection function to effectively provide articles that are likely to interest children and increase their engagement with the news.

[0056] The summarization function analyzes news articles, extracts key information, and summarizes it. For example, it can use AI to analyze news articles, extract key points, and summarize them. Specifically, it uses natural language processing technology to analyze the article's content and identify keywords and important phrases. The AI ​​learns a model to understand the context and structure of the article and extract key information. For example, it can summarize a long news article into a short and easy-to-understand format, displaying only the essential points. The summarization function performs summaries based on the length of the text and the importance of the information being summarized. For example, it might focus on the beginning or conclusion of the article, omitting detailed explanations and background information. Furthermore, the summarization function can adjust the difficulty and level of detail of the summary according to the child's age and comprehension level. For example, it can provide a short summary in simple language for preschoolers and a summary with slightly more detailed information for elementary school children. In addition, the summarization function can use illustrations and icons to visually present the summarized information in an easy-to-understand way. This allows the summarization function to help children easily understand the key points of news articles and increase their interest in the news.

[0057] The Learning Support Department provides quizzes and games related to news articles. For example, it can create quizzes related to news articles to check whether children understand the content. Specifically, it provides multiple-choice quizzes based on the content of news articles and fill-in-the-blank quizzes based on the content of the articles. This allows children to review the content of news articles after reading them and check their understanding. The Learning Support Department can also make learning news content fun through games. For example, it provides puzzle games related to news articles and storytelling games based on the content of the articles. This allows children to learn news content in an enjoyable way and solidify it in their memory. Furthermore, the Learning Support Department can track children's learning progress and provide appropriate feedback. For example, it records the correct answer rate for quizzes and the completion status of games to evaluate the child's understanding and learning progress. Based on this, it can suggest articles to read next or quizzes to try. In this way, the Learning Support Department allows children to learn news in an enjoyable way and deepen their understanding effectively.

[0058] The selection unit can estimate a child's emotions and adjust the criteria for selecting news articles based on the estimated emotions. For example, if the child is excited, the selection unit will prioritize articles with energetic content. If the child is depressed, the selection unit can also select articles with encouraging or uplifting content. Furthermore, if the child is relaxed, the selection unit can select articles that can be enjoyed in a relaxed state. In this way, by adjusting the criteria for selecting news articles according to the child's emotions, more appropriate news articles can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input the child's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0059] The selection unit can analyze a child's past browsing history and select the most suitable news articles. For example, the selection unit can prioritize articles from categories that the child has frequently viewed in the past. It can also analyze the trends of articles that the child has previously given high ratings to and select similar articles. Furthermore, the selection unit can extract the characteristics of articles that the child has viewed for extended periods in the past and select articles with similar characteristics. In this way, by analyzing past browsing history, the system can provide the most suitable news articles for the child. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the child's browsing history data into a generating AI and have the generating AI perform the selection of the most suitable news articles.

[0060] The selection unit can filter news articles based on the child's current learning progress and areas of interest. For example, the selection unit can select articles at an easily understandable level according to the child's learning progress. It can also prioritize relevant articles based on the child's areas of interest. Furthermore, the selection unit can select articles that will help the child achieve their learning goals. In this way, by filtering news articles based on learning progress and areas of interest, appropriate news articles can be provided to the child. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the child's learning progress data into a generating AI and have the generating AI perform the filtering of news articles.

[0061] The selection unit can estimate a child's emotions and determine the priority of news articles to select based on the estimated emotions. For example, if the child is excited, the selection unit will prioritize displaying articles with energetic content. If the child is depressed, the selection unit can also prioritize displaying articles with encouraging or uplifting content. Furthermore, if the child is relaxed, the selection unit can prioritize displaying articles that can be enjoyed in a relaxed state. In this way, by prioritizing news articles according to the child's emotions, more appropriate news articles can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input the child's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0062] The selection unit can prioritize selecting news articles that are highly relevant to the child, taking into account the child's geographical location. For example, the selection unit can prioritize news articles related to the child's area of ​​residence. Furthermore, if the child is traveling, the selection unit can prioritize news articles related to their travel destination. Additionally, the selection unit can prioritize news articles related to the child's school or local events. This allows the system to provide news articles that are highly relevant to the child by considering their geographical location. Some or all of the above processing in the selection unit may be performed using AI, or without AI. For example, the selection unit can input the child's geographical location data into a generating AI and have the generating AI select highly relevant news articles.

[0063] The selection unit can analyze a child's social media activity and select relevant articles when choosing news articles. For example, the selection unit can select articles related to topics that the child frequently shares on social media. It can also select articles based on the content of posts from accounts that the child follows on social media. Furthermore, the selection unit can select articles related to the areas of interest of groups that the child participates in on social media. In this way, by analyzing social media activity, it is possible to provide news articles that are highly relevant to the child. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the child's social media data into a generating AI and have the generating AI perform the selection of relevant news articles.

[0064] The summarization unit can estimate a child's emotions and adjust the way the summary is expressed based on the estimated emotions. For example, if the child is excited, the summarization unit will use energetic language in the summary. If the child is depressed, the summarization unit can also use encouraging and uplifting language in the summary. Furthermore, if the child is relaxed, the summarization unit can use relaxed language in the summary. By adjusting the way the summary is expressed according to the child's emotions, a more easily understandable summary can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input the child's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0065] The summarization unit can adjust the level of detail in the summary based on the importance of the news article during summary generation. For example, the summarization unit can provide a detailed summary for news articles of high importance, and a concise summary for news articles of low importance. Furthermore, the summarization unit can dynamically adjust the length and level of detail of the summary according to its importance. This allows for the provision of appropriate summaries by adjusting the level of detail based on the importance of the news article. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input news article importance data into a generation AI and have the generation AI perform the calculation of the level of detail in the summary.

[0066] The summarization unit can apply different summarization algorithms depending on the category of the news article when generating summaries. For example, the summarization unit can apply a summarization algorithm that simplifies technical terms to science articles. It can also apply a summarization algorithm that emphasizes match results to sports articles. Furthermore, it can apply a summarization algorithm that emphasizes storytelling to entertainment articles. By applying a summarization algorithm according to the category of the news article, it is possible to provide an appropriate summary. Some or all of the above processing in the summarization unit may be performed using AI, for example, or not using AI. For example, the summarization unit can input news article category data into a generating AI and have the generating AI perform the application of the summarization algorithm.

[0067] The summarization unit can estimate a child's emotions and adjust the length of the summary based on the estimated emotions. For example, if the child is excited, the summarization unit can provide a short, concise summary. If the child is depressed, the summarization unit can also provide a summary that includes encouraging and uplifting content. Furthermore, if the child is relaxed, the summarization unit can provide a longer summary that includes detailed explanations. By adjusting the length of the summary according to the child's emotions, a more easily understandable summary can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the summarization unit may be performed using AI, or not using AI. For example, the summarization unit can input the child's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0068] The summarization unit can determine the priority of summaries based on the publication date of news articles when generating summaries. For example, the summarization unit prioritizes summarizing the most recent news articles. The summarization unit can also adjust the priority of summaries for past news articles according to their importance. Furthermore, the summarization unit can dynamically adjust the order of summaries based on their publication date. This allows for the provision of appropriate summaries by determining the priority of summaries based on the publication date of news articles. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input news article publication date data into a generation AI and have the generation AI determine the priority of summaries.

[0069] The summarization unit can adjust the order of summaries based on the relevance of news articles during summary generation. For example, the summarization unit prioritizes summarizing highly relevant news articles. It can also summarize less relevant news articles concisely. Furthermore, the summarization unit can dynamically adjust the order of summaries based on relevance. This allows for the provision of appropriate summaries by adjusting the order of summaries based on the relevance of news articles. Some or all of the above processing in the summarization unit may be performed using AI, for example, or without AI. For example, the summarization unit can input news article relevance data into a generation AI and have the generation AI execute the order of summaries.

[0070] The learning support unit can estimate a child's emotions and adjust the difficulty of quizzes and games based on the estimated emotions. For example, if a child is excited, the learning support unit can provide a more difficult quiz or game. It can also provide an easier quiz or game if a child is depressed. Furthermore, if a child is relaxed, the learning support unit can provide a quiz or game of moderate difficulty. This allows for a more appropriate learning experience by adjusting the difficulty of quizzes and games according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning support unit may be performed using AI, or not. For example, the learning support unit can input a child's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0071] The learning support unit can select the most suitable questions for quizzes and games by referring to the child's past learning history. For example, the learning support unit can re-present questions that the child previously answered incorrectly. It can also prioritize questions in areas where the child has previously excelled. Furthermore, the learning support unit can select a balanced set of questions based on the child's learning history. In this way, by referring to past learning history, it can provide the child with the most suitable questions. Some or all of the above processing in the learning support unit may be performed using AI, for example, or not. For example, the learning support unit can input the child's learning history data into a generating AI and have the generating AI select the most suitable questions.

[0072] The learning support unit can apply different learning methods based on the content of news articles when creating quizzes and games. For example, in a quiz based on a science article, the learning support unit can ask questions about experimental results. In a quiz based on a history article, the learning support unit can also ask questions about dates and events. Furthermore, in a quiz based on an entertainment article, the learning support unit can ask questions about story comprehension. By applying different learning methods based on the content of news articles, more effective learning can be provided. Some or all of the above processing in the learning support unit may be performed using AI, for example, or without AI. For example, the learning support unit can input news article content data into a generating AI and have the generating AI execute the application of different learning methods.

[0073] The learning support unit can estimate a child's emotions and adjust the order of quizzes and games based on the estimated emotions. For example, if a child is excited, the learning support unit may present difficult questions first. Similarly, if a child is depressed, it may present easier questions first. Furthermore, if a child is relaxed, it may present questions of appropriate difficulty in a sequence. This allows for a more appropriate learning experience by adjusting the order of quizzes and games according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning support unit may be performed using AI, or not. For example, the learning support unit can input a child's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The learning support unit can prioritize presenting questions that are highly relevant to the child, taking into account the child's geographical location when presenting quizzes and games. For example, the learning support unit can prioritize questions related to the child's residential area. Furthermore, if the child is traveling, the learning support unit can prioritize questions related to their travel destination. It can also prioritize questions related to the child's school or local events. This allows the learning support unit to provide questions that are highly relevant to the child by considering their geographical location. Some or all of the above processing in the learning support unit may be performed using AI, or not. For example, the learning support unit can input the child's geographical location data into a generating AI and have the generating AI select highly relevant questions.

[0075] The learning support unit can improve the accuracy of quizzes and games by referring to related literature in news articles. For example, the learning support unit can create questions by referring to academic papers related to news articles. It can also create questions by referring to books related to news articles. Furthermore, the learning support unit can create questions by referring to websites related to news articles. In this way, the accuracy of the questions can be improved by referring to related literature. Some or all of the above processing in the learning support unit may be performed using AI, for example, or without AI. For example, the learning support unit can input related literature data for news articles into a generating AI and have the generating AI perform the task of improving the accuracy of the questions.

[0076] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0077] The children's news summary platform may also include a text-to-speech function. This function provides the ability to read news articles aloud. For example, it can provide news articles in audio format to children who have difficulty reading or who are visually impaired. The text-to-speech function can also read the news articles at a slower pace to make them easier for children to understand. Furthermore, the text-to-speech function can highlight important points in the news articles, making them easier for children to comprehend. Some or all of the above processing in the text-to-speech function may be performed using AI, or not. For example, the text-to-speech function can input the text data of the news article into a generating AI and have the generating AI perform the text-to-speech.

[0078] A news summarization platform for children can also include an emotional feedback unit. This unit records the child's emotions after reading a news article and uses this information to select future articles. For example, it can provide feedback such as "it was fun" or "it was boring" after the child reads an article. The emotional feedback unit can also analyze the child's emotional feedback and prioritize articles the child found enjoyable in future selections. Furthermore, it can adjust the selection criteria to avoid articles the child found boring. This allows for the optimization of news article selection based on the child's emotions. Emotional feedback can be implemented using, for example, an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the emotional feedback unit may be performed using AI or not. For example, the emotional feedback unit can input the child's emotional data into a generative AI and have the generative AI perform the emotional feedback analysis.

[0079] The news summarization platform for children may also include an interactive question-and-answer section. This section provides a function that allows children to interactively answer questions related to news articles. For example, after reading a news article, questions about its content can be posed to the child, and their understanding can be checked based on their answers. The interactive question-and-answer section can also provide additional information and explanations based on the child's answers. Furthermore, the interactive question-and-answer section can provide a mechanism where children earn points for answering questions, thereby increasing their motivation to learn. This allows for a deeper understanding of the news article's content and promotes learning. Some or all of the above processing in the interactive question-and-answer section may be performed using AI, for example, or without AI. For example, the interactive question-and-answer section could input news article content data into a generating AI and have the generating AI generate the questions and answers.

[0080] The children's news summary platform can further use sentiment estimation to adjust the display format of news articles based on a child's emotions. For example, if a child is excited, a colorful and dynamic display format can be used. If a child is depressed, a calm color scheme can be used. Furthermore, if a child is relaxed, a simple and easy-to-read display format can be used. This allows for a more comfortable browsing experience by adjusting the display format of news articles according to a child's emotions. Sentiment estimation is achieved using sentiment estimation functionality, such as using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in adjusting the display format may be performed using AI or not. For example, the display format adjustment can be performed by inputting the child's emotion data into the generative AI and having the generative AI perform the adjustment.

[0081] A news summary platform for children can also include a function to automatically display relevant videos and images based on the content of news articles. For example, it can automatically search for videos related to news articles and display them next to the article. It can also automatically display images related to news articles. Furthermore, it can display relevant infographics and charts based on the content of news articles. This visually complements the content of news articles, making them easier for children to understand. Some or all of the above processes for displaying relevant videos and images may be performed using AI, for example, or not. For example, for displaying relevant videos and images, news article content data could be input into a generating AI, and the generating AI could be made to search for and display relevant media.

[0082] The children's news summarization platform can further adjust the difficulty level of news articles based on a child's emotions using sentiment estimation functionality. For example, if a child is excited, it can display articles with a higher difficulty level. If a child is depressed, it can display articles that are easier to understand. Furthermore, if a child is relaxed, it can display articles of a moderate difficulty level. This allows for a more appropriate learning experience by adjusting the difficulty level of news articles according to a child's emotions. Sentiment estimation is achieved using sentiment estimation functionality, for example, with a sentiment engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in adjusting the difficulty level of news articles may be performed using AI, or not using AI. For example, the difficulty level of news articles can be adjusted by inputting a child's emotion data into a generative AI and having the generative AI perform the difficulty adjustment.

[0083] A news summary platform for children can also include a function to recommend related books and materials based on the content of news articles. For example, it can automatically search for books related to news articles and recommend them to children. It can also recommend learning materials and websites related to news articles. Furthermore, it can recommend related documentaries and movies based on the content of news articles. This can provide resources for deeper learning about the content of news articles and support children's learning. Some or all of the above processes in recommending related books and materials may be performed using AI, for example, or not using AI. For example, for recommending related books and materials, news article content data can be input into a generating AI, and the generating AI can be made to search for and display recommended resources.

[0084] The news summarization platform for children can further use sentiment estimation capabilities to adjust how news articles are summarized based on a child's emotions. For example, if a child is excited, it can provide a short summary that highlights the main points. If a child is depressed, it can provide a summary that includes encouraging and uplifting content. Furthermore, if a child is relaxed, it can provide a longer summary that includes detailed explanations. This allows for more easily understandable summaries by adjusting how news articles are summarized according to a child's emotions. Sentiment estimation is achieved using sentiment estimation capabilities, such as using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in adjusting how news articles are summarized may be performed using AI or not. For example, the adjustment of how news articles are summarized can be done by inputting a child's emotion data into a generative AI and having the generative AI perform the adjustment.

[0085] A news summary platform for children can also include a function to suggest relevant experiments and activities based on the content of news articles. For example, it could suggest experiments related to science articles, allowing children to deepen their learning by actually performing the experiments. It could also suggest activities related to history articles, allowing children to experience historical events. Furthermore, it could suggest activities related to entertainment articles, allowing children to learn while having fun. This allows children to learn the content of news articles through real-world experiences, thereby increasing their motivation to learn. Some or all of the above-mentioned processes in suggesting relevant experiments and activities may be performed using AI, for example, or not. For example, the suggestion of relevant experiments and activities could be done by inputting the content data of the news article into a generating AI, and having the generating AI perform the generation of the suggested content.

[0086] The children's news summarization platform can further use sentiment estimation to adjust the display order of news articles based on the child's emotions. For example, if the child is excited, articles with energetic content can be displayed first. If the child is depressed, articles with encouraging or uplifting content can be displayed first. Furthermore, if the child is relaxed, articles that can be enjoyed in a relaxed state can be displayed first. This allows for the provision of more appropriate news articles by adjusting the display order according to the child's emotions. Sentiment estimation is achieved using sentiment estimation functionality, for example, with a sentiment engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in adjusting the display order of news articles may be performed using AI or not. For example, the adjustment of the display order of news articles can be performed by inputting the child's emotion data into a generative AI and having the generative AI perform the adjustment.

[0087] The following briefly describes the processing flow for example form 2.

[0088] Step 1: The selection function chooses news articles based on the child's interests and age. The selection function can identify a child's interests, for example, through surveys or past behavioral data. It can also categorize news articles by age group and select appropriate articles. For example, it can display news articles about animals to children interested in animals, and news articles about science to children interested in science. Step 2: The summarization unit analyzes the news article, extracts important information, and summarizes it. The summarization unit can, for example, use AI to analyze the news article, extract the key points, and summarize them. The summarization unit performs summarization based on the length of the text and the importance of the information being summarized. For example, it can summarize a long news article into a short and easy-to-understand format, displaying only the important points. Step 3: The learning support department provides quizzes and games related to news articles. For example, the learning support department can create quizzes related to news articles to check whether children understand the content of the news. The learning support department can also make learning about the news fun through games. For example, it can provide puzzle games related to news articles so that children can learn about the news in an enjoyable way.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] Each of the multiple elements described above, including the selection unit, summarization unit, and learning support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart device 14 and selects news articles based on the child's interests and age. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes news articles, extracts important information, and summarizes it. The learning support unit is implemented by the control unit 46A of the smart device 14 and provides quizzes and games related to the news articles. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0093] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the selection unit, summarization unit, and learning support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart glasses 214 and selects news articles based on the child's interests and age. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes news articles, extracts important information, and summarizes it. The learning support unit is implemented by the control unit 46A of the smart glasses 214 and provides quizzes and games related to the news articles. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0109] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the selection unit, summarization unit, and learning support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the headset terminal 314 and selects news articles based on the child's interests and age. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes news articles, extracts important information, and summarizes it. The learning support unit is implemented by the control unit 46A of the headset terminal 314 and provides quizzes and games related to the news articles. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements, including the selection unit, summarization unit, and learning support unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the robot 414 and selects news articles based on the child's interests and age. The summarization unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes news articles, extracts important information, and summarizes it. The learning support unit is implemented by the control unit 46A of the robot 414 and provides quizzes and games related to the news articles. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] (Note 1) A selection section where news articles are chosen based on the child's interests and age, A summarization unit that summarizes the news articles selected by the selection unit, The learning support unit provides quizzes and games based on news articles summarized by the summarization unit. A system characterized by the following features. (Note 2) The aforementioned selection unit is We estimate children's emotions and adjust the criteria for selecting news articles based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned selection unit is Analyze a child's past browsing history to select the most relevant news articles. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is When selecting news articles, filter them based on the child's current learning progress and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned selection unit is The system estimates children's emotions and prioritizes news articles based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned selection unit is When selecting news articles, prioritize articles that are highly relevant, taking into account the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned selection unit is When selecting news articles, the system analyzes the child's social media activity and selects relevant articles. The system described in Appendix 1, characterized by the features described herein. (Note 8) The summary section above is, The system estimates the child's emotions and adjusts the way the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The summary section above is, When generating summaries, adjust the level of detail in the summary based on the importance of the news article. The system described in Appendix 1, characterized by the features described herein. (Note 10) The summary section above is, When generating summaries, different summarization algorithms are applied depending on the category of the news article. The system described in Appendix 1, characterized by the features described herein. (Note 11) The summary section above is, The system estimates the child's emotions and adjusts the length of the summary based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The summary section above is, When generating summaries, the priority of summaries is determined based on the publication date of the news articles. The system described in Appendix 1, characterized by the features described herein. (Note 13) The summary section above is, When generating summaries, the order of the summaries is adjusted based on the relevance of the news articles. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning support unit is: The system estimates the child's emotions and adjusts the difficulty of quizzes and games based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning support unit is: When creating quizzes or games, the system selects the most suitable questions by referring to the child's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning support unit is: When creating quizzes or games, different learning methods are applied based on the content of news articles. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning support unit is: The system estimates the child's emotions and adjusts the order of questions in quizzes and games based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning support unit is: When creating quizzes or games, prioritize questions that are highly relevant to the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning support unit is: When creating quiz or game questions, we refer to related articles in news reports to improve the accuracy of the questions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0161] 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. A selection section where news articles are chosen based on the child's interests and age, A summarization unit that summarizes the news articles selected by the selection unit, The learning support unit provides quizzes and games based on news articles summarized by the summarization unit. A system characterized by the following features.

2. The aforementioned selection unit is We estimate children's emotions and adjust the criteria for selecting news articles based on those estimated emotions. The system according to feature 1.

3. The aforementioned selection unit is Analyze a child's past browsing history to select the most relevant news articles. The system according to feature 1.

4. The aforementioned selection unit is When selecting news articles, filter them based on the child's current learning progress and areas of interest. The system according to feature 1.

5. The aforementioned selection unit is The system estimates children's emotions and prioritizes news articles based on those estimated emotions. The system according to feature 1.

6. The aforementioned selection unit is When selecting news articles, prioritize articles that are highly relevant, taking into account the child's geographical location. The system according to feature 1.

7. The aforementioned selection unit is When selecting news articles, the system analyzes the child's social media activity and selects relevant articles. The system according to feature 1.

8. The summary section above is, The system estimates the child's emotions and adjusts the way the summary is presented based on those estimated emotions. The system according to feature 1.

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