System

Abstract

An object of a system according to an embodiment is to enable a child to safely use the Internet.SOLUTION: A system includes a site analysis part, a filtering part, a summarization part, and a conversion part. The site analysis unit analyzes the contents of the site. The filtering unit filters inappropriate contents from the contents analyzed by the site analysis unit. The summarizing unit summarizes the contents filtered by the filtering unit according to the grade and the learning level. The conversion unit converts the content summarized by the summarization unit into an expression in which the use of Chinese characters is adjusted.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a risk that children would access inappropriate content when using the Internet, and there was also the issue that information was not provided adequately according to grade level or learning level.

[0005] The system according to the embodiment aims to enable children to use the Internet safely. [Means for solving the problem]

[0006] The system according to the embodiment includes a site analysis unit, a filtering unit, a summarization unit, and a conversion unit. The site analysis unit analyzes the content of the site. The filtering unit filters inappropriate content from the content analyzed by the site analysis unit. The summarization unit summarizes the content filtered by the filtering unit according to grade level or learning level. The conversion unit converts the content summarized by the summarization unit into an expression that adjusts the use of kanji. [Effects of the Invention]

[0007] The system according to the embodiment can enable children to use the Internet safely. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The safe internet connection system according to the embodiment of the present invention is a system that analyzes websites accessed by children, filters inappropriate content, and adjusts the use of summaries and kanji according to the grade and learning level, thereby providing an environment in which children can use the internet safely.

[0029] A secure Internet connection system according to an embodiment includes a site analysis unit, a filtering unit, a summarization unit, and a conversion unit. The site analysis unit analyzes the content of sites accessed by children. For example, the site analysis unit analyzes text data to understand the content of the site. The site analysis unit can also analyze the content of images and videos. The filtering unit filters inappropriate content from the content analyzed by the site analysis unit. For example, the filtering unit detects and filters violent content and adult content. The filtering unit can also filter inappropriate content, including content at linked sites. The summarization unit summarizes the content filtered by the filtering unit according to grade level or learning level. For example, the summarization unit summarizes in simple terms for elementary school children and provides more detailed information for middle school children. The summarization unit can also customize the summary content based on the child's interests. The conversion unit converts the content summarized by the summarization unit into an expression that adjusts the use of kanji. For example, the conversion unit uses a lot of hiragana and katakana for younger children and uses appropriate kanji for older children. Furthermore, the conversion unit can automatically generate annotations that supplement the readings and meanings of kanji. As a result, the secure Internet connection system according to the embodiment can provide an environment in which children can safely use the Internet. For example, when children use the Internet for learning, they can obtain appropriate information according to their grade level and learning level without being exposed to inappropriate content. Furthermore, children can safely enjoy using entertainment sites. Furthermore, when children use the Internet for research or study, they can efficiently collect information.

[0030] The site analysis unit can also analyze the content of images and videos on a site to filter inappropriate visual content. For example, the site analysis unit uses a generation AI to analyze image data on a site to detect and filter violent scenes or adult images. For example, image recognition technology can be used to identify inappropriate images based on specific patterns or colors. In addition, when analyzing video content, the generation AI checks the content frame by frame to detect and filter inappropriate scenes. For example, scenes containing violent acts or inappropriate language can be automatically cut. In addition, the site analysis unit uses a generation AI to analyze the entire visual content of a site and filter inappropriate content based on specific keywords and tags. For example, images and videos tagged as adult can be automatically blocked. This makes it possible to filter inappropriate content in images and videos.

[0031] The site analysis unit also tracks links on a site and can filter out inappropriate content, including the content of the links. For example, the generation AI tracks links within a site and analyzes the content of the linked pages to detect inappropriate content. For example, if the linked page contains violent content, the link is disabled. The site analysis unit also analyzes links on a site in real time and filters out inappropriate content, including updates to the linked content. For example, it can also handle cases where the linked page is updated and new inappropriate content is added. The site analysis unit also periodically checks links on a site and re-analyzes them if the content of the linked page changes, filtering out inappropriate content. For example, it automatically re-analyzes them when the linked page is changed. This makes it possible to filter out inappropriate content at linked pages.

[0032] The filtering unit can also analyze the audio content of the site and filter out inappropriate audio content. For example, the generation AI analyzes the audio data of the site and detects and filters inappropriate words and expressions. For example, it blocks audio containing violent language or adult content. The filtering unit also uses speech recognition technology to analyze the audio content of the site in real time with the generation AI and detect inappropriate content. For example, it analyzes the audio stream and automatically mutes inappropriate parts. The filtering unit also analyzes the entire audio content of the site with the generation AI and filters out inappropriate content based on specific keywords or phrases. For example, it automatically blocks audio containing specific inappropriate phrases. This makes it possible to filter out inappropriate content in audio content.

[0033] The filtering unit tracks the update history of a site and can filter out inappropriate content, including past content. For example, the generation AI tracks the update history of a site and detects inappropriate content, including past content. For example, if violent content has been included in the past, it will filter out the content based on that history. The filtering unit also periodically analyzes the update history of a site and, if past content is inappropriate, retains that information and filters it in conjunction with the current content. For example, it prevents inappropriate content from being displayed again in the past. The filtering unit also builds a system in which the generation AI tracks the update history of a site in real time and displays a warning if past content is inappropriate. For example, it displays a warning when accessing a site that has previously contained inappropriate content. This makes it possible to filter out inappropriate content from the past.

[0034] The summarization unit can customize the summary content based on a child's interests and concerns. For example, the generation AI analyzes a child's interests and concerns and customizes the summary content based on that. For example, for a child who is interested in animals, information related to animals will be emphasized. The summarization unit also customizes the summary content based on the child's past browsing history and search history. For example, if a child is interested in a particular topic, information related to that topic will be prioritized for summary. The summarization unit also analyzes a child's interests and concerns in real time and dynamically customizes the summary content based on the results. For example, information related to a topic that a child has recently become interested in will be instantly summarized. This allows the summary content to be customized based on a child's interests and concerns.

[0035] The summarizing section can provide additional information and reference materials related to the summary content. For example, when the generation AI creates a summary, the summarizing section automatically provides additional information and reference materials related to the summary content. For example, it displays links and literature related to the summarized content. The summarizing section can also automatically generate visual materials and charts related to the summary content and provide them in a form that is easy for children to understand. For example, it can display illustrations and graphs related to the summary content. The summarizing section can also provide video and audio materials related to the summary content so that children can learn from multiple angles. For example, it can display educational videos and audio commentaries related to the summary content. This allows the provision of additional information and reference materials related to the summary content.

[0036] The summarization unit can provide opportunities for children of different grades or learning levels to compare summaries and learn from each other. For example, the summarization unit constructs a system in which the generation AI allows children of different grades or learning levels to compare summaries and provide opportunities for them to learn from each other. For example, the summarization unit shares summaries and promotes discussions. The summarization unit also provides the generation AI with a function that allows children of different grades or learning levels to compare summaries and provide mutual feedback. For example, it allows them to post comments and questions about the summaries. The summarization unit also provides opportunities for children of different grades or learning levels to compare summaries and work together on learning projects. For example, it can conduct joint surveys and research based on the summaries. This can provide opportunities for children of different grades or learning levels to learn from each other.

[0037] The summarization unit can generate illustrations and diagrams to visually represent the summary content. For example, the summarization unit uses a generation AI to automatically generate illustrations and diagrams to visually represent the summary content. For example, it provides illustrations depicting characters and scenes related to the summary content. The summarization unit also uses a generation AI to generate related charts and graphs to make the summary content easier to understand visually. For example, it provides graphs that visually display data and statistical information. The summarization unit also uses a generation AI to generate infographics to visually represent the summary content, allowing children to intuitively understand it. For example, it provides infographics that illustrate the summary content. This allows the summary content to be visually represented.

[0038] The conversion unit references the child's kanji learning history and prioritizes the use of kanji that have already been learned. For example, the conversion unit builds a system in which the generation AI references the child's kanji learning history and prioritizes the use of kanji that have already been learned. For example, it converts text based on a list of learned kanji. The conversion unit also analyzes the child's kanji learning history in real time, and the generation AI selects and uses appropriate kanji. For example, it adjusts the frequency of kanji use according to the child's learning progress. The conversion unit also provides a function in which the generation AI adds furigana to kanji that have not yet been learned based on the child's kanji learning history. For example, it automatically adds furigana to kanji that have not yet been learned. This allows the generation AI to prioritize the use of learned kanji.

[0039] The conversion unit can automatically generate annotations that supplement the reading and meaning of kanji. For example, the generation AI automatically generates annotations that supplement the reading and meaning of kanji and provides them in a format that is easy for children to understand. For example, the reading and meaning are displayed next to the kanji. Furthermore, when adjusting the use of kanji, the conversion unit provides a function where the generation AI provides an audio explanation of the reading and meaning of kanji. For example, when the mouse is hovered over a kanji, the reading and meaning are explained audio. Furthermore, when the generation AI adjusts the use of kanji, the conversion unit automatically generates infographics that illustrate the reading and meaning of kanji. For example, it provides materials that illustrate the origins and meanings of kanji. This makes it possible to automatically generate annotations that supplement the reading and meaning of kanji.

[0040] The conversion unit can translate into different languages ​​when adjusting the use of kanji, and generate expressions that are compatible with multiple languages. For example, when the generation AI adjusts the use of kanji, the conversion unit automatically translates into different languages ​​and generates expressions that are compatible with multiple languages. For example, it translates Japanese kanji into English or French. In addition, the conversion unit allows the generation AI to analyze the meaning and context of the kanji and perform an appropriate translation to generate expressions that are compatible with multiple languages. For example, it provides an appropriate translation taking into account the meaning of the kanji. In addition, the conversion unit translates into different languages ​​when the generation AI adjusts the use of kanji, and provides an environment where children can learn in multiple languages. For example, it translates Japanese kanji into Spanish or Chinese. This makes it possible to generate expressions that are compatible with multiple languages.

[0041] The conversion unit can generate animations showing the stroke order and stroke order of kanji. For example, the conversion unit uses a generation AI to automatically generate animations showing the stroke order and stroke order of kanji, allowing children to learn the correct way to write them. For example, the conversion unit displays the stroke order of kanji in an animation. When adjusting the use of kanji, the conversion unit also uses a generation AI to provide interactive animations showing the stroke order and stroke order. For example, it provides an animation that allows children to actually trace the stroke order. The conversion unit also uses a generation AI to generate animations showing the stroke order and stroke order of kanji, providing an environment in which children can learn visually. For example, it provides an animation that shows the stroke order of kanji step by step. This allows children to visually learn the stroke order and stroke order of kanji.

[0042] The system can analyze a child's internet usage history and improve safety based on usage patterns. For example, the system uses a generating AI to analyze a child's internet usage history and build a system that improves safety based on usage patterns. For example, it can restrict access to inappropriate sites during certain times of the day. The system can also analyze a child's internet usage history in real time, and the generating AI can make suggestions to improve safety. For example, it can block access to certain sites. The system can also provide a safe internet connection based on a child's internet usage history and in line with their usage patterns. For example, it can restrict access to entertainment sites during study hours. This makes it possible to improve safety based on usage patterns.

[0043] The system can provide a dashboard that allows parents and teachers to monitor their children's internet usage. For example, the system is constructed so that the generating AI provides a dashboard that allows parents and teachers to monitor their children's internet usage. For example, it displays the history of sites that the child has accessed. The system also provides a dashboard that allows parents and teachers to monitor their children's internet usage in real time. For example, it displays the sites that the child is currently accessing. The system also provides a dashboard that allows parents and teachers to monitor their children's internet usage and displays alerts according to usage. For example, it displays an alert when an attempt is made to access an inappropriate site. This allows parents and teachers to monitor their children's internet usage.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The system can analyze a child's internet usage history and improve safety based on usage patterns. For example, a generating AI can analyze a child's internet usage history and build a system that improves safety based on usage patterns. For example, it can restrict access to inappropriate sites during certain times of the day. The system can also analyze a child's internet usage history in real time, and the generating AI can make suggestions to improve safety. For example, it can block access to certain sites. The system can also provide a safe internet connection based on a child's internet usage history and in line with their usage patterns. For example, it can restrict access to entertainment sites during study hours. This makes it possible to improve safety based on usage patterns.

[0046] The system can provide a dashboard that allows parents and teachers to monitor their children's internet usage. For example, a system can be constructed in which a generating AI provides a dashboard that allows parents and teachers to monitor their children's internet usage. For example, it can display the history of sites that a child has accessed. The system can also provide a dashboard that allows parents and teachers to monitor their children's internet usage in real time. For example, it can display the sites that a child is currently accessing. The system can also provide a dashboard that allows parents and teachers to monitor their children's internet usage and display alerts based on usage. For example, it can display an alert when an attempt is made to access an inappropriate site. This allows parents and teachers to monitor their children's internet usage.

[0047] The system can analyze a child's internet usage history and improve safety based on usage patterns. For example, a generating AI can analyze a child's internet usage history and build a system that improves safety based on usage patterns. For example, it can restrict access to inappropriate sites during certain times of the day. The system can also analyze a child's internet usage history in real time, and the generating AI can make suggestions to improve safety. For example, it can block access to certain sites. The system can also provide a safe internet connection based on a child's internet usage history and in line with their usage patterns. For example, it can restrict access to entertainment sites during study hours. This makes it possible to improve safety based on usage patterns.

[0048] The system can provide a dashboard that allows parents and teachers to monitor their children's internet usage. For example, a system can be constructed in which a generating AI provides a dashboard that allows parents and teachers to monitor their children's internet usage. For example, it can display the history of sites that a child has accessed. The system can also provide a dashboard that allows parents and teachers to monitor their children's internet usage in real time. For example, it can display the sites that a child is currently accessing. The system can also provide a dashboard that allows parents and teachers to monitor their children's internet usage and display alerts based on usage. For example, it can display an alert when an attempt is made to access an inappropriate site. This allows parents and teachers to monitor their children's internet usage.

[0049] The system can analyze a child's internet usage history and improve safety based on usage patterns. For example, a generating AI can analyze a child's internet usage history and build a system that improves safety based on usage patterns. For example, it can restrict access to inappropriate sites during certain times of the day. The system can also analyze a child's internet usage history in real time, and the generating AI can make suggestions to improve safety. For example, it can block access to certain sites. The system can also provide a safe internet connection based on a child's internet usage history and in line with their usage patterns. For example, it can restrict access to entertainment sites during study hours. This makes it possible to improve safety based on usage patterns.

[0050] The system can provide a dashboard that allows parents and teachers to monitor their children's internet usage. For example, a system can be constructed in which a generating AI provides a dashboard that allows parents and teachers to monitor their children's internet usage. For example, it can display the history of sites that a child has accessed. The system can also provide a dashboard that allows parents and teachers to monitor their children's internet usage in real time. For example, it can display the sites that a child is currently accessing. The system can also provide a dashboard that allows parents and teachers to monitor their children's internet usage and display alerts based on usage. For example, it can display an alert when an attempt is made to access an inappropriate site. This allows parents and teachers to monitor their children's internet usage.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The site analysis unit analyzes the content of the site that the child accesses. For example, the site analysis unit analyzes text data to understand the content of the site. The site analysis unit can also analyze the content of images and videos. Step 2: The filtering unit filters out inappropriate content from the content analyzed by the site analysis unit. For example, the filtering unit detects and filters out violent content or adult content. The filtering unit can also filter out inappropriate content, including the content of linked pages. Step 3: The summarizing unit summarizes the content filtered by the filtering unit according to the grade level or learning level. For example, the summarizing unit summarizes in simple terms for elementary school students and provides more detailed information for middle school students. The summarizing unit can also customize the summary content based on the child's interests. Step 4: The converter converts the content summarized by the summarizer into a representation that adjusts the use of kanji. For example, the converter may use more hiragana and katakana for younger children and more appropriate kanji for older children. The converter can also automatically generate annotations that supplement the readings and meanings of kanji.

[0053] (Example 2) The safe internet connection system according to the embodiment of the present invention is a system that analyzes websites accessed by children, filters inappropriate content, and adjusts the use of summaries and kanji according to the grade and learning level, thereby providing an environment in which children can use the internet safely.

[0054] A secure Internet connection system according to an embodiment includes a site analysis unit, a filtering unit, a summarization unit, and a conversion unit. The site analysis unit analyzes the content of sites accessed by children. For example, the site analysis unit analyzes text data to understand the content of the site. The site analysis unit can also analyze the content of images and videos. The filtering unit filters inappropriate content from the content analyzed by the site analysis unit. For example, the filtering unit detects and filters violent content and adult content. The filtering unit can also filter inappropriate content, including content at linked sites. The summarization unit summarizes the content filtered by the filtering unit according to grade level or learning level. For example, the summarization unit summarizes in simple terms for elementary school children and provides more detailed information for middle school children. The summarization unit can also customize the summary content based on the child's interests. The conversion unit converts the content summarized by the summarization unit into an expression that adjusts the use of kanji. For example, the conversion unit uses a lot of hiragana and katakana for younger children and uses appropriate kanji for older children. Furthermore, the conversion unit can automatically generate annotations that supplement the readings and meanings of kanji. As a result, the secure Internet connection system according to the embodiment can provide an environment in which children can safely use the Internet. For example, when children use the Internet for learning, they can obtain appropriate information according to their grade level and learning level without being exposed to inappropriate content. Furthermore, children can safely enjoy using entertainment sites. Furthermore, when children use the Internet for research or study, they can efficiently collect information.

[0055] The site analysis unit can also analyze the content of images and videos on a site to filter inappropriate visual content. For example, the site analysis unit uses a generation AI to analyze image data on a site to detect and filter violent scenes or adult images. For example, image recognition technology can be used to identify inappropriate images based on specific patterns or colors. In addition, when analyzing video content, the generation AI checks the content frame by frame to detect and filter inappropriate scenes. For example, scenes containing violent acts or inappropriate language can be automatically cut. In addition, the site analysis unit uses a generation AI to analyze the entire visual content of a site and filter inappropriate content based on specific keywords and tags. For example, images and videos tagged as adult can be automatically blocked. This makes it possible to filter inappropriate content in images and videos.

[0056] The site analysis unit also tracks links on a site and can filter out inappropriate content, including the content of the links. For example, the generation AI tracks links within a site and analyzes the content of the linked pages to detect inappropriate content. For example, if the linked page contains violent content, the link is disabled. The site analysis unit also analyzes links on a site in real time and filters out inappropriate content, including updates to the linked content. For example, it can also handle cases where the linked page is updated and new inappropriate content is added. The site analysis unit also periodically checks links on a site and re-analyzes them if the content of the linked page changes, filtering out inappropriate content. For example, it automatically re-analyzes them when the linked page is changed. This makes it possible to filter out inappropriate content at linked pages.

[0057] The site analysis unit uses the emotion estimation function to evaluate the emotional impact of site content on children and filter content that may evoke negative emotions. For example, the site analysis unit uses generative AI to analyze the text data of a site and perform emotion analysis to detect content that may evoke negative emotions. For example, it filters out expressions that evoke sadness or anger. The site analysis unit also uses the emotion estimation function to evaluate the emotional impact of the site's image and video content and filter out visual content that may evoke negative emotions. For example, it blocks images and videos that evoke fear. The site analysis unit also performs emotion analysis on links on a site and disables links if the content of the linked content is likely to evoke negative emotions. For example, it responds to cases where the linked content contains content that evokes anxiety. This makes it possible to filter out content that evokes negative emotions.

[0058] The filtering unit can also analyze the audio content of the site and filter out inappropriate audio content. For example, the generation AI analyzes the audio data of the site and detects and filters inappropriate words and expressions. For example, it blocks audio containing violent language or adult content. The filtering unit also uses speech recognition technology to analyze the audio content of the site in real time with the generation AI and detect inappropriate content. For example, it analyzes the audio stream and automatically mutes inappropriate parts. The filtering unit also analyzes the entire audio content of the site with the generation AI and filters out inappropriate content based on specific keywords or phrases. For example, it automatically blocks audio containing specific inappropriate phrases. This makes it possible to filter out inappropriate content in audio content.

[0059] The filtering unit tracks the update history of a site and can filter out inappropriate content, including past content. For example, the generation AI tracks the update history of a site and detects inappropriate content, including past content. For example, if violent content has been included in the past, it will filter out the content based on that history. The filtering unit also periodically analyzes the update history of a site and, if past content is inappropriate, retains that information and filters it in conjunction with the current content. For example, it prevents inappropriate content from being displayed again in the past. The filtering unit also builds a system in which the generation AI tracks the update history of a site in real time and displays a warning if past content is inappropriate. For example, it displays a warning when accessing a site that has previously contained inappropriate content. This makes it possible to filter out inappropriate content from the past.

[0060] The filtering unit can use the emotion estimation function to make suggestions to enhance the positive emotions that the content of the site inspires in children. For example, the filtering unit uses a generative AI to analyze the text data of the site and perform emotion analysis to make suggestions to enhance content that evokes positive emotions. For example, it recommends expressions that evoke encouragement and joy. The filtering unit also uses the emotion estimation function to evaluate the emotional impact of the image and video content of the site and makes suggestions to enhance visual content that evokes positive emotions. For example, it recommends images and videos that evoke smiles and joy. The filtering unit also performs emotion analysis on links on the site, and if the content of the linked content evokes positive emotions, it makes suggestions to strengthen the link. For example, it makes recommendations if the linked content contains moving content. This makes it possible to strengthen content that evokes positive emotions.

[0061] The summarization unit can customize the summary content based on a child's interests and concerns. For example, the generation AI analyzes a child's interests and concerns and customizes the summary content based on that. For example, for a child who is interested in animals, information related to animals will be emphasized. The summarization unit also customizes the summary content based on the child's past browsing history and search history. For example, if a child is interested in a particular topic, information related to that topic will be prioritized for summary. The summarization unit also analyzes a child's interests and concerns in real time and dynamically customizes the summary content based on the results. For example, information related to a topic that a child has recently become interested in will be instantly summarized. This allows the summary content to be customized based on a child's interests and concerns.

[0062] The summarizing section can provide additional information and reference materials related to the summary content. For example, when the generation AI creates a summary, the summarizing section automatically provides additional information and reference materials related to the summary content. For example, it displays links and literature related to the summarized content. The summarizing section can also automatically generate visual materials and charts related to the summary content and provide them in a form that is easy for children to understand. For example, it can display illustrations and graphs related to the summary content. The summarizing section can also provide video and audio materials related to the summary content so that children can learn from multiple angles. For example, it can display educational videos and audio commentaries related to the summary content. This allows the provision of additional information and reference materials related to the summary content.

[0063] The summarization unit can provide opportunities for children of different grades or learning levels to compare summaries and learn from each other. For example, the summarization unit constructs a system in which the generation AI allows children of different grades or learning levels to compare summaries and provide opportunities for them to learn from each other. For example, the summarization unit shares summaries and promotes discussions. The summarization unit also provides the generation AI with a function that allows children of different grades or learning levels to compare summaries and provide mutual feedback. For example, it allows them to post comments and questions about the summaries. The summarization unit also provides opportunities for children of different grades or learning levels to compare summaries and work together on learning projects. For example, it can conduct joint surveys and research based on the summaries. This can provide opportunities for children of different grades or learning levels to learn from each other.

[0064] The summarization unit can generate illustrations and diagrams to visually represent the summary content. For example, the summarization unit uses a generation AI to automatically generate illustrations and diagrams to visually represent the summary content. For example, it provides illustrations depicting characters and scenes related to the summary content. The summarization unit also uses a generation AI to generate related charts and graphs to make the summary content easier to understand visually. For example, it provides graphs that visually display data and statistical information. The summarization unit also uses a generation AI to generate infographics to visually represent the summary content, allowing children to intuitively understand it. For example, it provides infographics that illustrate the summary content. This allows the summary content to be visually represented.

[0065] The summarization unit can use the emotion estimation function to make suggestions to reinforce the positive emotions that the summary content inspires in children. For example, the summarization unit uses a generation AI to perform emotion analysis of the summary content and make suggestions to generate summaries that evoke positive emotions. For example, it provides summaries that use expressions that evoke encouragement and joy. The summarization unit also uses the emotion estimation function to evaluate the emotional impact that the summary content has on children and generate summaries that evoke positive emotions. For example, it provides summaries that evoke a sense of success and accomplishment. The summarization unit also builds a system that generates summaries that evoke positive emotions based on the emotion analysis results of the summary content. For example, it prioritizes providing summaries with high emotion scores. This makes it possible to generate summaries that evoke positive emotions.

[0066] The conversion unit references the child's kanji learning history and prioritizes the use of kanji that have already been learned. For example, the conversion unit builds a system in which the generation AI references the child's kanji learning history and prioritizes the use of kanji that have already been learned. For example, it converts text based on a list of learned kanji. The conversion unit also analyzes the child's kanji learning history in real time, and the generation AI selects and uses appropriate kanji. For example, it adjusts the frequency of kanji use according to the child's learning progress. The conversion unit also provides a function in which the generation AI adds furigana to kanji that have not yet been learned based on the child's kanji learning history. For example, it automatically adds furigana to kanji that have not yet been learned. This allows the generation AI to prioritize the use of learned kanji.

[0067] The conversion unit can automatically generate annotations that supplement the reading and meaning of kanji. For example, the generation AI automatically generates annotations that supplement the reading and meaning of kanji and provides them in a format that is easy for children to understand. For example, the reading and meaning are displayed next to the kanji. Furthermore, when adjusting the use of kanji, the conversion unit provides a function where the generation AI provides an audio explanation of the reading and meaning of kanji. For example, when the mouse is hovered over a kanji, the reading and meaning are explained audio. Furthermore, when the generation AI adjusts the use of kanji, the conversion unit automatically generates infographics that illustrate the reading and meaning of kanji. For example, it provides materials that illustrate the origins and meanings of kanji. This makes it possible to automatically generate annotations that supplement the reading and meaning of kanji.

[0068] The conversion unit can use the emotion estimation function to evaluate the emotional impact that the use of kanji has on children and generate expressions that evoke positive emotions. For example, the conversion unit uses a generation AI to perform emotion analysis on the use of kanji and generate expressions that evoke positive emotions. For example, it recommends the use of kanji that make children feel joy or a sense of accomplishment. The conversion unit also uses the emotion estimation function to evaluate the emotional impact that the use of kanji has on children and generates expressions that evoke positive emotions. For example, it recommends the use of kanji that make children feel a sense of success. The conversion unit also builds a system that generates expressions that evoke positive emotions based on the results of the emotion analysis on the use of kanji. For example, it preferentially recommends the use of kanji with a high emotion score. This makes it possible to promote the use of kanji that evoke positive emotions.

[0069] The conversion unit can translate into different languages ​​when adjusting the use of kanji, and generate expressions that are compatible with multiple languages. For example, when the generation AI adjusts the use of kanji, the conversion unit automatically translates into different languages ​​and generates expressions that are compatible with multiple languages. For example, it translates Japanese kanji into English or French. In addition, the conversion unit allows the generation AI to analyze the meaning and context of the kanji and perform an appropriate translation to generate expressions that are compatible with multiple languages. For example, it provides an appropriate translation taking into account the meaning of the kanji. In addition, the conversion unit translates into different languages ​​when the generation AI adjusts the use of kanji, and provides an environment where children can learn in multiple languages. For example, it translates Japanese kanji into Spanish or Chinese. This makes it possible to generate expressions that are compatible with multiple languages.

[0070] The conversion unit can generate animations showing the stroke order and stroke order of kanji. For example, the conversion unit uses a generation AI to automatically generate animations showing the stroke order and stroke order of kanji, allowing children to learn the correct way to write them. For example, the conversion unit displays the stroke order of kanji in an animation. When adjusting the use of kanji, the conversion unit also uses a generation AI to provide interactive animations showing the stroke order and stroke order. For example, it provides an animation that allows children to actually trace the stroke order. The conversion unit also uses a generation AI to generate animations showing the stroke order and stroke order of kanji, providing an environment in which children can learn visually. For example, it provides an animation that shows the stroke order of kanji step by step. This allows children to visually learn the stroke order and stroke order of kanji.

[0071] The conversion unit can use the emotion estimation function to make suggestions to reinforce the positive emotions that the use of kanji inspires in children. For example, the conversion unit uses a generation AI to perform an emotion analysis on the use of kanji and recommends the use of kanji that evoke positive emotions. For example, it recommends the use of kanji that make children feel joyful or a sense of accomplishment. The conversion unit also uses the emotion estimation function to evaluate the emotional impact that the use of kanji has on children and recommends the use of kanji that evoke positive emotions. For example, it recommends the use of kanji that evoke a sense of success. The conversion unit also builds a system that recommends the use of kanji that evoke positive emotions based on the results of the emotion analysis on the use of kanji. For example, it prioritizes the recommendation of the use of kanji with a high emotion score. This makes it possible to promote the use of kanji that evoke positive emotions.

[0072] The system can analyze a child's internet usage history and improve safety based on usage patterns. For example, the system uses a generating AI to analyze a child's internet usage history and build a system that improves safety based on usage patterns. For example, it can restrict access to inappropriate sites during certain times of the day. The system can also analyze a child's internet usage history in real time, and the generating AI can make suggestions to improve safety. For example, it can block access to certain sites. The system can also provide a safe internet connection based on a child's internet usage history and in line with their usage patterns. For example, it can restrict access to entertainment sites during study hours. This makes it possible to improve safety based on usage patterns.

[0073] The system uses an emotion estimation function to monitor a child's emotional state while using the Internet and can automatically block sites that may evoke negative emotions. For example, the system uses the emotion estimation function to build a system in which a generation AI monitors a child's emotional state while using the Internet and automatically blocks sites that may evoke negative emotions. For example, it blocks sites that make children feel anxious. The system also uses the emotion estimation function to monitor a child's emotional state while using the Internet in real time and automatically blocks sites that may evoke negative emotions. For example, it blocks sites that make children feel scared. The system also provides a function in which a generation AI uses the emotion estimation function to monitor a child's emotional state while using the Internet and automatically blocks sites that may evoke negative emotions. For example, it blocks sites that make children feel anger. This makes it possible to automatically block sites that evoke negative emotions.

[0074] The system can provide a dashboard that allows parents and teachers to monitor their children's internet usage. For example, the system is constructed so that the generating AI provides a dashboard that allows parents and teachers to monitor their children's internet usage. For example, it displays the history of sites that the child has accessed. The system also provides a dashboard that allows parents and teachers to monitor their children's internet usage in real time. For example, it displays the sites that the child is currently accessing. The system also provides a dashboard that allows parents and teachers to monitor their children's internet usage and displays alerts according to usage. For example, it displays an alert when an attempt is made to access an inappropriate site. This allows parents and teachers to monitor their children's internet usage.

[0075] The system can use the emotion estimation function to make suggestions to reinforce positive emotions in children while using the Internet. For example, the system uses the emotion estimation function to monitor the emotional state of children while using the Internet and recommend sites and content that evoke positive emotions. For example, it recommends sites that children find enjoyable. The system also uses the emotion estimation function to analyze the emotional state of children while using the Internet in real time and provide content that evokes positive emotions. For example, it recommends sites related to topics that interest children. The system also uses the emotion estimation function to monitor the emotional state of children while using the Internet and make suggestions to evoke positive emotions. For example, it recommends content that helps children relax. This makes it possible to recommend sites and content that evoke positive emotions.

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

[0077] The system can analyze a child's internet usage history and improve safety based on usage patterns. For example, a generating AI can analyze a child's internet usage history and build a system that improves safety based on usage patterns. For example, it can restrict access to inappropriate sites during certain times of the day. The system can also analyze a child's internet usage history in real time, and the generating AI can make suggestions to improve safety. For example, it can block access to certain sites. The system can also provide a safe internet connection based on a child's internet usage history and in line with their usage patterns. For example, it can restrict access to entertainment sites during study hours. This makes it possible to improve safety based on usage patterns.

[0078] The system can provide a dashboard that allows parents and teachers to monitor their children's internet usage. For example, a system can be constructed in which a generating AI provides a dashboard that allows parents and teachers to monitor their children's internet usage. For example, it can display the history of sites that a child has accessed. The system can also provide a dashboard that allows parents and teachers to monitor their children's internet usage in real time. For example, it can display the sites that a child is currently accessing. The system can also provide a dashboard that allows parents and teachers to monitor their children's internet usage and display alerts based on usage. For example, it can display an alert when an attempt is made to access an inappropriate site. This allows parents and teachers to monitor their children's internet usage.

[0079] The system uses the emotion estimation function to monitor a child's emotional state while using the internet and can automatically block sites that may evoke negative emotions. For example, a system can be built in which a generation AI uses the emotion estimation function to monitor a child's emotional state while using the internet and automatically block sites that may evoke negative emotions. For example, it can block sites that make a child feel anxious. The system also uses the emotion estimation function to monitor a child's emotional state while using the internet in real time and automatically block sites that may evoke negative emotions. For example, it can block sites that make a child feel scared. The system also provides a function in which a generation AI uses the emotion estimation function to monitor a child's emotional state while using the internet and automatically block sites that may evoke negative emotions. For example, it can block sites that make a child feel angry. This makes it possible to automatically block sites that evoke negative emotions.

[0080] The system can use the emotion estimation function to make suggestions to reinforce positive emotions in children while using the Internet. For example, the generation AI uses the emotion estimation function to monitor a child's emotional state while using the Internet and recommend sites and content that evoke positive emotions. For example, it can recommend sites that children find enjoyable. The system also uses the emotion estimation function to analyze a child's emotional state while using the Internet in real time and provide content that evokes positive emotions. For example, it can recommend sites related to topics that interest the child. The system also uses the emotion estimation function to monitor a child's emotional state while using the Internet and make suggestions to evoke positive emotions. For example, it can recommend content that helps children relax. This makes it possible to recommend sites and content that evoke positive emotions.

[0081] The system can analyze a child's internet usage history and improve safety based on usage patterns. For example, a generating AI can analyze a child's internet usage history and build a system that improves safety based on usage patterns. For example, it can restrict access to inappropriate sites during certain times of the day. The system can also analyze a child's internet usage history in real time, and the generating AI can make suggestions to improve safety. For example, it can block access to certain sites. The system can also provide a safe internet connection based on a child's internet usage history and in line with their usage patterns. For example, it can restrict access to entertainment sites during study hours. This makes it possible to improve safety based on usage patterns.

[0082] The system can provide a dashboard that allows parents and teachers to monitor their children's internet usage. For example, a system can be constructed in which a generating AI provides a dashboard that allows parents and teachers to monitor their children's internet usage. For example, it can display the history of sites that a child has accessed. The system can also provide a dashboard that allows parents and teachers to monitor their children's internet usage in real time. For example, it can display the sites that a child is currently accessing. The system can also provide a dashboard that allows parents and teachers to monitor their children's internet usage and display alerts based on usage. For example, it can display an alert when an attempt is made to access an inappropriate site. This allows parents and teachers to monitor their children's internet usage.

[0083] The system uses the emotion estimation function to monitor a child's emotional state while using the internet and can automatically block sites that may evoke negative emotions. For example, a system can be built in which a generation AI uses the emotion estimation function to monitor a child's emotional state while using the internet and automatically block sites that may evoke negative emotions. For example, it can block sites that make a child feel anxious. The system also uses the emotion estimation function to monitor a child's emotional state while using the internet in real time and automatically block sites that may evoke negative emotions. For example, it can block sites that make a child feel scared. The system also provides a function in which a generation AI uses the emotion estimation function to monitor a child's emotional state while using the internet and automatically block sites that may evoke negative emotions. For example, it can block sites that make a child feel angry. This makes it possible to automatically block sites that evoke negative emotions.

[0084] The system can use the emotion estimation function to make suggestions to reinforce positive emotions in children while using the Internet. For example, the generation AI uses the emotion estimation function to monitor a child's emotional state while using the Internet and recommend sites and content that evoke positive emotions. For example, it can recommend sites that children find enjoyable. The system also uses the emotion estimation function to analyze a child's emotional state while using the Internet in real time and provide content that evokes positive emotions. For example, it can recommend sites related to topics that interest the child. The system also uses the emotion estimation function to monitor a child's emotional state while using the Internet and make suggestions to evoke positive emotions. For example, it can recommend content that helps children relax. This makes it possible to recommend sites and content that evoke positive emotions.

[0085] The system can analyze a child's internet usage history and improve safety based on usage patterns. For example, a generating AI can analyze a child's internet usage history and build a system that improves safety based on usage patterns. For example, it can restrict access to inappropriate sites during certain times of the day. The system can also analyze a child's internet usage history in real time, and the generating AI can make suggestions to improve safety. For example, it can block access to certain sites. The system can also provide a safe internet connection based on a child's internet usage history and in line with their usage patterns. For example, it can restrict access to entertainment sites during study hours. This makes it possible to improve safety based on usage patterns.

[0086] The system can provide a dashboard that allows parents and teachers to monitor their children's internet usage. For example, a system can be constructed in which a generating AI provides a dashboard that allows parents and teachers to monitor their children's internet usage. For example, it can display the history of sites that a child has accessed. The system can also provide a dashboard that allows parents and teachers to monitor their children's internet usage in real time. For example, it can display the sites that a child is currently accessing. The system can also provide a dashboard that allows parents and teachers to monitor their children's internet usage and display alerts based on usage. For example, it can display an alert when an attempt is made to access an inappropriate site. This allows parents and teachers to monitor their children's internet usage.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The site analysis unit analyzes the content of the site that the child accesses. For example, the site analysis unit analyzes text data to understand the content of the site. The site analysis unit can also analyze the content of images and videos. Step 2: The filtering unit filters out inappropriate content from the content analyzed by the site analysis unit. For example, the filtering unit detects and filters out violent content or adult content. The filtering unit can also filter out inappropriate content, including the content of linked pages. Step 3: The summarizing unit summarizes the content filtered by the filtering unit according to the grade level or learning level. For example, the summarizing unit summarizes in simple terms for elementary school students and provides more detailed information for middle school students. The summarizing unit can also customize the summary content based on the child's interests. Step 4: The converter converts the content summarized by the summarizer into a representation that adjusts the use of kanji. For example, the converter may use more hiragana and katakana for younger children and more appropriate kanji for older children. The converter can also automatically generate annotations that supplement the readings and meanings of kanji.

[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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0117] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0130] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0133] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0139] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0140] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0141] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0142] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0143] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0144] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0145] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0146] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0147] 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.

[0148] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0149] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0150] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0151] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0152] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0153] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0154] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0155] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a site analysis unit that analyzes the content of the site; a filtering unit that filters inappropriate content from the content analyzed by the site analysis unit; a summarizing unit that summarizes the content filtered by the filtering unit according to a grade or learning level; a conversion unit that converts the content summarized by the summarization unit into an expression that adjusts the use of kanji characters; A system characterized by:

2. The site analysis unit It also analyzes the content of images and videos on your site to filter out inappropriate visual content.

2. The system of claim 1.

3. The site analysis unit Track links on the site and filter out inappropriate content, including the content of those links.

2. The system of claim 1.

4. The site analysis unit Evaluate the emotional impact of site content on children and filter content that may provoke negative emotions 2. The system of claim 1.

5. The filtering unit It also analyzes the audio content of the site and filters out inappropriate audio content.

2. The system of claim 1.

Images