system

Abstract

The system according to this embodiment aims to automatically identify and block inappropriate content so that children can safely enjoy video streaming sites. [Solution] The system according to this embodiment comprises a content analysis unit, an age restriction unit, a display unit, a keyword filtering unit, and an image recognition unit. The content analysis unit analyzes the content of a video streaming site in real time. The age restriction unit sets age restrictions based on the data analyzed by the content analysis unit. The display unit displays only content suitable for a specific age group based on the age restrictions set by the age restriction unit. The keyword filtering unit automatically blocks content containing specific keywords or phrases. The image recognition unit identifies and blocks inappropriate images or scenes in the video.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, inappropriate content has not been sufficiently identified and blocked for children to safely enjoy video distribution sites, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically identify and block inappropriate content for children to safely enjoy video distribution sites.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a content analysis unit, an age restriction unit, a display unit, a keyword filtering unit, and an image recognition unit. The content analysis unit analyzes the content of a video streaming site in real time. The age restriction unit sets age restrictions based on the data analyzed by the content analysis unit. The display unit displays only content suitable for a specific age group based on the age restrictions set by the age restriction unit. The keyword filtering unit automatically blocks content containing specific keywords or phrases. The image recognition unit identifies and blocks inappropriate images or scenes in the video. [Effects of the Invention]

[0007] The system according to this embodiment can automatically identify and block inappropriate content so that children can safely enjoy video streaming sites. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An automated blocking system according to an embodiment of the present invention is a system for children to safely enjoy video streaming sites. This automated blocking system uses machine learning to identify inappropriate content in real time and automatically block it. Specifically, it combines content analysis, age restrictions, keyword filtering, and image recognition to provide a safety net that parents can customize according to their child's age and sensitivities. First, the automated blocking system analyzes the content of video streaming sites in real time. For example, the automated blocking system analyzes the content of videos and detects inappropriate words and phrases. Next, the automated blocking system sets age restrictions and displays only content suitable for specific age groups. Furthermore, the automated blocking system performs keyword filtering and automatically blocks content containing specific keywords or phrases. Finally, the automated blocking system uses image recognition technology to identify and block inappropriate images and scenes in videos. This automated blocking system is customizable by parents based on their child's age and sensitivities. For example, parents can set specific keywords or phrases and filter content based on them. Parents can also analyze their child's viewing history and trends to provide optimal filtering settings. This automated blocking system allows children to view safe and wholesome content, and parents can confidently manage their children's online activities. For example, if a child attempts to view inappropriate content, the automatic blocking system will automatically block it and notify the parent. This eliminates the need for parents to monitor all content, thus ensuring the safety of their children. In this way, the automatic blocking system allows children to enjoy video streaming sites safely.

[0029] The automated blocking system according to this embodiment comprises a content analysis unit, an age restriction unit, a display unit, a keyword filtering unit, and an image recognition unit. The content analysis unit analyzes the content of a video streaming site in real time. For example, the content analysis unit analyzes the content of a video and detects inappropriate words and phrases. For example, the content analysis unit can analyze the text data of a video using natural language processing technology to identify inappropriate words. The content analysis unit can also transcribe the audio in the video into text using speech recognition technology to detect inappropriate phrases. Furthermore, the content analysis unit can analyze the video footage using image recognition technology to identify inappropriate scenes. The age restriction unit sets age restrictions based on the data analyzed by the content analysis unit. For example, the age restriction unit sets age restrictions to display only content suitable for a specific age group. For example, the age restriction unit can filter content suitable for a specific age group based on age restrictions set by parents. The age restriction unit can also set age restrictions based on regulations specific to each country or region. Furthermore, the age restriction unit can adjust the age restriction criteria based on the user's feelings. The display unit shows only content suitable for a specific age group based on age restrictions set by the age restriction unit. The display unit can, for example, display content filtered based on age restrictions. The display unit can, for example, select content to display based on the user's emotions. The display unit can also display highly relevant content based on the user's viewing history. Furthermore, the display unit can select the optimal display method based on the user's device information. The keyword filtering unit automatically blocks content containing specific keywords or phrases. The keyword filtering unit filters content based on specific keywords or phrases set by parents, for example. The keyword filtering unit can, for example, analyze the meaning of keywords using natural language processing technology to improve the accuracy of filtering. The keyword filtering unit can also improve the accuracy of filtering by considering synonyms and related words.Furthermore, the keyword filtering unit can adjust the filtering criteria based on the user's emotions. The image recognition unit identifies and blocks inappropriate images and scenes in the video. For example, the image recognition unit identifies inappropriate scenes in the video using image recognition technology. For example, the image recognition unit can identify inappropriate people using facial recognition technology. The image recognition unit can also identify inappropriate objects using object recognition technology. Furthermore, the image recognition unit can adjust the image recognition criteria based on the user's emotions. As a result, the automatic blocking system according to this embodiment allows children to safely enjoy video streaming sites. Some or all of the above-described processes in the content analysis unit, age restriction unit, display unit, keyword filtering unit, and image recognition unit may be performed using AI, for example, or without AI. For example, the content analysis unit can analyze the content of a video using generative AI to analyze the content of a video streaming site in real time and detect inappropriate words and phrases. The age restriction unit can set age restrictions using generative AI and display only content suitable for a specific age group. The display unit can select content to display using generative AI and display content suitable for a specific age group. The keyword filtering unit can automatically block content containing specific keywords or phrases using generative AI. The image recognition unit can identify and block inappropriate images or scenes within a video using generative AI.

[0030] The Content Analysis Department analyzes content from video streaming sites in real time. Specifically, it uses natural language processing technology to analyze video content and detect inappropriate words and phrases. For example, it can identify inappropriate words and phrases by transcribing the audio from a video into text and analyzing that text data. By using speech recognition technology, it is possible to transcribe the audio in a video into text in real time and analyze that text data. Furthermore, the Content Analysis Department can also analyze the video content using image recognition technology to identify inappropriate scenes. For example, if a particular scene contains violent acts or inappropriate images, it can detect this and issue a warning. In this way, the Content Analysis Department can comprehensively analyze the text data, audio data, and video data of a video and detect inappropriate content in real time. Furthermore, by using generative AI, it is possible to analyze video content more advancedly and detect inappropriate words and phrases with greater accuracy. Generative AI has the ability to learn from large amounts of data and understand context and nuances, so it can identify inappropriate expressions that cannot be detected by simple keyword matching. For example, generative AI can analyze the content of a video and determine whether a particular word is inappropriate in context. This enables the content analysis unit to detect inappropriate content with greater accuracy, providing users with a safer viewing environment.

[0031] The age restriction unit sets age restrictions based on data analyzed by the content analysis unit. Specifically, the age restriction unit sets age restrictions to display only content suitable for a particular age group. For example, it can filter content suitable for a particular age group based on age restrictions set by parents. Furthermore, the age restriction unit can also set age restrictions based on country- or regional regulations. For example, if certain content is considered inappropriate for minors in a particular country, it can set age restrictions according to that country's regulations. The age restriction unit can also adjust age restriction criteria based on user sentiment. For example, if a user expresses discomfort with certain content, the age restriction criteria can be reviewed based on that feedback. Moreover, by using generative AI, the age restriction unit can set more sophisticated age restrictions. Generative AI has the ability to deeply understand the content and determine whether it is appropriate for a particular age group. For example, generative AI can analyze the content of a video and determine whether it is inappropriate for a particular age group. This allows the age restriction unit to set more accurate age restrictions and provide users with a safer viewing environment.

[0032] The display unit shows only content suitable for a specific age group based on age restrictions set by the age restriction unit. Specifically, the display unit displays content filtered based on age restrictions. For example, content deemed inappropriate based on age restrictions is not displayed, and only content deemed appropriate is shown. The display unit can also select content to display based on the user's emotions. For example, if a user shows a positive reaction to a particular piece of content, similar content can be displayed to that user. Furthermore, the display unit can also display highly relevant content based on the user's viewing history. For example, it can recommend highly relevant content based on content the user has viewed in the past. In addition, the display unit can select the optimal display method based on the user's device information. For example, it can select the optimal display method according to the device the user is using, such as a smartphone, tablet, or PC, to provide the user with a comfortable viewing experience. By using generative AI, the display unit can perform more sophisticated content selection. Generative AI has the ability to comprehensively analyze the user's viewing history, emotions, and device information to select the most suitable content. This allows the display unit to provide users with a more personalized viewing experience and improve satisfaction.

[0033] The keyword filtering unit automatically blocks content containing specific keywords or phrases. Specifically, it filters content based on specific keywords or phrases set by parents. For example, content containing certain inappropriate words or phrases is automatically blocked. The keyword filtering unit can also improve filtering accuracy by analyzing the meaning of keywords using natural language processing technology. For example, it can achieve more accurate filtering by considering synonyms and related words. Furthermore, the keyword filtering unit can adjust filtering criteria based on user sentiment. For example, if a user expresses discomfort with a particular keyword, the filtering criteria can be reviewed based on that feedback. By using generative AI, the keyword filtering unit can perform more sophisticated filtering. Generative AI has the ability to learn from large amounts of data and understand context and nuances, so it can identify inappropriate expressions that cannot be detected by simple keyword matching. For example, generative AI can analyze the content of a video and determine whether a particular keyword is inappropriate in context. As a result, the keyword filtering unit can achieve more accurate filtering of inappropriate content and provide users with a safe viewing environment.

[0034] The image recognition unit identifies and blocks inappropriate images and scenes within a video. Specifically, the image recognition unit uses image recognition technology to identify inappropriate scenes within a video. For example, if a particular scene contains violent acts or inappropriate images, it can detect them and issue a warning. The image recognition unit can identify inappropriate individuals using facial recognition technology. For example, if a particular person appears in a video, it can determine whether that person is engaging in inappropriate behavior. The image recognition unit can also identify inappropriate objects using object recognition technology. For example, if a particular object appears in a video, it can determine whether that object is inappropriate. Furthermore, the image recognition unit can adjust its image recognition criteria based on the user's emotions. For example, if a user expresses discomfort with a particular scene, the image recognition criteria can be reviewed based on that feedback. By using generative AI, the image recognition unit can perform more advanced image recognition. Generative AI has the ability to learn from large amounts of data and deeply understand scenes and objects within videos, so it can identify inappropriate scenes and objects that cannot be detected by simple image recognition. For example, generative AI can analyze the content of a video and determine whether a particular scene is contextually inappropriate. This enables the image recognition unit to identify and block inappropriate content with greater accuracy, providing users with a safer viewing environment.

[0035] The customization section allows parents to set specific keywords or phrases and filter content based on them. For example, the customization section filters content based on specific keywords or phrases set by the parents. For example, the customization section can identify content to be filtered using a list of keywords set by the parents. The customization section can also identify content to be filtered using a list of phrases set by the parents. Furthermore, the customization section can adjust the filtering criteria based on the keywords or phrases set by the parents. This allows parents to customize the filtering settings to suit their child's age and sensitivities. Some or all of the above processing in the customization section may be performed using AI, for example, or not using AI. For example, the customization section can input a list of keywords set by the parents into a generating AI and identify content to be filtered.

[0036] The analysis unit can analyze a child's viewing history and trends and provide optimal filtering settings for parents. For example, the analysis unit can analyze a child's viewing history and identify viewing trends. For example, the analysis unit can use viewing history data to analyze a child's viewing trends. The analysis unit can also provide optimal filtering settings based on viewing history data. Furthermore, the analysis unit can adjust the filtering criteria using viewing history data. This allows parents to provide optimal filtering settings based on their child's viewing history and trends. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a child's viewing history data into a generating AI and have the generating AI perform the analysis of viewing trends.

[0037] The content analysis unit can analyze the content of a video and detect inappropriate words and phrases. For example, the content analysis unit can analyze the text data of the video using natural language processing technology to identify inappropriate words. For example, the content analysis unit can analyze the content of a video using text generation AI (e.g., LLM) to detect inappropriate words and phrases. Furthermore, the content analysis unit can transcribe the audio in the video into text using speech recognition technology to detect inappropriate phrases. For example, the content analysis unit can transcribe the conversations in the video into text using speech recognition technology to identify inappropriate phrases. In addition, the content analysis unit can analyze the video footage using image recognition technology to identify inappropriate scenes. As a result, the content analysis unit can detect and block inappropriate words and phrases by analyzing the content of the video. Some or all of the above-described processes in the content analysis unit may be performed using AI, or not. For example, the content analysis unit can use generation AI to detect inappropriate words and phrases in order to analyze the content of a video.

[0038] The age restriction unit can set age restrictions to display only content suitable for a specific age group. For example, the age restriction unit can filter content suitable for a specific age group based on age restrictions set by parents. The age restriction unit can also set age restrictions based on country or region regulations. For example, the age restriction unit can refer to country-specific age restriction regulations and set appropriate age restrictions. Furthermore, the age restriction unit can adjust age restriction criteria based on user emotions. For example, if a user is experiencing stress, the age restriction unit can set stricter age restriction criteria. This ensures that children can safely view content by displaying only content suitable for a specific age group. Some or all of the above processing in the age restriction unit may be performed using AI, for example, or not. For example, the age restriction unit can use generative AI to filter content suitable for a specific age group in order to set age restrictions.

[0039] The keyword filtering unit can automatically block content containing specific keywords or phrases. For example, the keyword filtering unit filters content based on specific keywords or phrases set by parents. For example, the keyword filtering unit can identify content to be filtered using a keyword list set by parents. It can also identify content to be filtered using a phrase list set by parents. Furthermore, the keyword filtering unit can improve filtering accuracy by analyzing the meaning of keywords using natural language processing technology. For example, the keyword filtering unit can analyze the context of keywords using natural language processing technology to identify keywords with inappropriate meanings. This allows for the elimination of inappropriate content by automatically blocking content containing specific keywords or phrases. Some or all of the above-described processes in the keyword filtering unit may be performed using AI, or not. For example, the keyword filtering unit can use generative AI to automatically block content containing specific keywords or phrases in order to perform keyword filtering.

[0040] The image recognition unit can identify and block inappropriate images and scenes within a video. For example, the image recognition unit can identify inappropriate scenes within a video using image recognition technology. The image recognition unit can identify inappropriate people using facial recognition technology. For example, the image recognition unit can identify inappropriate people within a video using facial recognition technology. The image recognition unit can also identify inappropriate objects using object recognition technology. For example, the image recognition unit can identify inappropriate objects within a video using object recognition technology. Furthermore, the image recognition unit can adjust the image recognition criteria based on the user's emotions. For example, if the user is feeling stressed, the image recognition unit can set stricter image recognition criteria. This allows children to watch safely by identifying and blocking inappropriate images and scenes within the video. Some or all of the above-described processes in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can use generative AI to identify and block inappropriate images and scenes within a video in order to perform image recognition.

[0041] The content analysis unit can use speech recognition technology to detect inappropriate words and phrases when analyzing the content of a video. For example, the content analysis unit can use speech recognition technology to transcribe the audio in the video into text and detect inappropriate phrases. For example, the content analysis unit can use speech recognition technology to transcribe the conversations in the video into text and identify inappropriate phrases. The content analysis unit can also use speech recognition technology to detect inappropriate elements from the music and sound effects in the video. For example, the content analysis unit can use speech recognition technology to analyze the music and sound effects in the video and identify inappropriate elements. This makes it easier to detect inappropriate words and phrases in a video by using speech recognition technology. Some or all of the above processing in the content analysis unit may be performed using AI, for example, or without AI. For example, the content analysis unit can use generative AI to perform speech recognition technology in order to analyze the content of a video and detect inappropriate words and phrases.

[0042] The content analysis unit can analyze subtitle data to detect inappropriate words and phrases when analyzing the content of a video. For example, the content analysis unit can analyze the video's subtitle data in real time to detect inappropriate words. For example, the content analysis unit can perform text analysis on the subtitle data to identify inappropriate phrases. The content analysis unit can also use the subtitle data to analyze the conversation content within the video in detail and detect inappropriate elements. For example, the content analysis unit can use the subtitle data to analyze the conversation content within the video in detail and identify inappropriate elements. This makes it easier to detect inappropriate words and phrases within a video by analyzing the subtitle data. Some or all of the above-described processes in the content analysis unit may be performed using AI, for example, or without AI. For example, the content analysis unit can use generative AI to analyze subtitle data in order to analyze the content of a video and detect inappropriate words and phrases.

[0043] The content analysis unit can improve the efficiency of analysis by adjusting the video playback speed when analyzing the content of a video. For example, the content analysis unit can speed up the video playback speed to perform analysis quickly. For example, by speeding up the video playback speed, the content analysis unit can analyze a large amount of content in a short time. The content analysis unit can also slow down the video playback speed to perform detailed analysis. For example, by slowing down the video playback speed, the content analysis unit can perform detailed analysis down to the smallest detail. Furthermore, the content analysis unit can perform efficient analysis by appropriately adjusting the video playback speed. For example, by appropriately adjusting the video playback speed, the content analysis unit can perform efficient and accurate analysis. In this way, the efficiency of analysis can be improved by adjusting the video playback speed. Some or all of the above processes in the content analysis unit may be performed using AI, for example, or without using AI. For example, the content analysis unit can perform efficient analysis by adjusting the video playback speed using generative AI in order to analyze the content of a video.

[0044] The content analysis unit can improve the accuracy of its analysis by referring to the video's metadata when analyzing the video's content. For example, the content analysis unit can improve the accuracy of its analysis by referring to the video's metadata to identify the content's category. For example, the content analysis unit can improve the accuracy of its analysis by using the video's metadata to extract relevant keywords. Furthermore, the content analysis unit can improve the accuracy of its analysis by considering the video's length and publication date based on the video's metadata. For example, the content analysis unit can improve the accuracy of its analysis by considering the video's length and publication date based on the video's metadata. In this way, the accuracy of the analysis can be improved by referring to the video's metadata. Some or all of the above processes in the content analysis unit may be performed using AI, for example, or without AI. For example, the content analysis unit can improve the accuracy of its analysis by using generative AI to refer to the video's metadata in order to analyze the video's content.

[0045] The age restriction unit can set age restrictions by taking into account regulations specific to each country and region. For example, the age restriction unit can refer to age restriction regulations specific to each country and set appropriate age restrictions. The age restriction unit can also set age restrictions by taking into account the culture and customs specific to each region. Furthermore, the age restriction unit can set age restrictions by referring to international age restriction standards. The age restriction unit can set age restrictions by referring to international age restriction standards. This allows for the setting of appropriate age restrictions by taking into account regulations specific to each country and region. Some or all of the above processing in the age restriction unit may be performed using AI, for example, or not using AI. For example, the age restriction unit can use generative AI to set age restrictions by taking into account regulations specific to each country and region.

[0046] The age restriction unit can apply different criteria depending on the video genre when setting age restrictions. For example, it can apply lenient age restrictions to educational videos. It can also apply strict age restrictions to highly entertaining videos. Furthermore, it can apply moderate age restrictions to news and documentary videos. This allows for more appropriate age restrictions by applying different criteria depending on the video genre. Some or all of the above processing in the age restriction unit may be performed using AI, for example, or not. For example, the age restriction unit can use generative AI to apply different criteria depending on the video genre in order to set age restrictions.

[0047] The age restriction unit can set the optimal age restriction by referring to the user's past viewing history. For example, the age restriction unit can analyze the user's past viewing history and set an appropriate age restriction. The age restriction unit can also set age restrictions based on the user's past viewing habits. Furthermore, the age restriction unit can adjust age restrictions for specific genres based on the user's viewing history. This allows for the setting of the optimal age restriction by referring to the user's past viewing history. Some or all of the above processing in the age restriction unit may be performed using AI, for example, or without AI. For example, the age restriction unit can use generative AI to refer to the user's past viewing history and set the optimal restriction in order to set age restrictions.

[0048] The age restriction unit can customize age restrictions based on specific conditions set by parents when setting age restrictions. For example, the age restriction unit can adjust age restrictions based on specific keywords set by parents. The age restriction unit can also set age restrictions based on viewing time limits set by parents. Furthermore, the age restriction unit can customize age restrictions for specific genres set by parents. This allows for more appropriate restrictions by customizing age restrictions based on specific conditions set by parents. Some or all of the above processing in the age restriction unit may be performed using AI, for example, or not. For example, the age restriction unit can use generative AI to customize restrictions based on specific conditions set by parents in order to set age restrictions.

[0049] The display unit can display the most suitable content based on the video viewing time when selecting content to display. For example, the display unit can prioritize displaying content suitable for short viewing times. The display unit can also display content suitable for long viewing times. Furthermore, the display unit can display balanced content according to viewing time. This improves the user's viewing experience by displaying the most suitable content based on the video viewing time. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use generative AI to display the most suitable content based on the video viewing time in order to select content to display.

[0050] The display unit can select content to display by referring to video ratings and comments. For example, the display unit can prioritize displaying highly-rated content. The display unit can also prioritize displaying content with many positive comments. Furthermore, the display unit can display balanced content based on ratings and comments. This allows the display unit to show content that is beneficial to the user by referring to video ratings and comments. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use a generating AI to refer to video ratings and comments in order to select content to display.

[0051] The display unit can select the optimal display method by considering the user's device information when selecting content to display. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. The display unit can also provide a display method optimized for a larger screen if the user is using a tablet. Furthermore, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows the display unit to provide the optimal display method by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use generative AI to select the optimal display method by considering the user's device information in order to select content to display.

[0052] The display unit can analyze the user's viewing history to select content to display and show highly relevant content. For example, the display unit can analyze the user's past viewing history and show highly relevant content. The display unit can also prioritize displaying content in genres that the user frequently watches. Furthermore, the display unit can display content that the user might be interested in based on their viewing history. In this way, by analyzing the user's viewing history, highly relevant content can be provided. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use generative AI to analyze the user's viewing history and display highly relevant content in order to select content to display.

[0053] The keyword filtering unit can analyze the meaning of keywords using natural language processing techniques when performing keyword filtering. For example, the keyword filtering unit can analyze the context of keywords using natural language processing techniques to identify keywords with inappropriate meanings. Furthermore, the keyword filtering unit can analyze the ambiguity of keywords using natural language processing techniques to perform appropriate filtering. In addition, the keyword filtering unit can analyze the emotional nuances of keywords using natural language processing techniques to improve the accuracy of filtering. This allows for the analysis of keyword meanings and improvement of filtering accuracy by using natural language processing techniques. Some or all of the above-described processes in the keyword filtering unit may be performed using AI, for example, or without AI. For example, the keyword filtering unit can perform keyword filtering by executing natural language processing techniques using generative AI to analyze the meaning of keywords.

[0054] The keyword filtering unit can improve the accuracy of keyword filtering by considering synonyms and related words. For example, the keyword filtering unit can expand the keywords to be filtered using a synonym dictionary. Furthermore, the keyword filtering unit can also expand the keywords to be filtered using a thesaurus. The keyword filtering unit can also improve the accuracy of filtering by considering synonyms and related words. This allows for improved filtering accuracy by considering synonyms and related words. Some or all of the above processing in the keyword filtering unit may be performed using AI, for example, or without AI. For example, the keyword filtering unit can improve the accuracy of keyword filtering by considering synonyms and related words using generative AI.

[0055] The keyword filtering unit can set the most suitable keywords by referring to the user's past search history when performing keyword filtering. For example, the keyword filtering unit can analyze the user's past search history and set the keywords to be filtered. The keyword filtering unit can also set the keywords to be filtered based on keywords that the user frequently searches for. The keyword filtering unit can also extract highly relevant keywords from the user's search history and set them to be filtered. This allows the system to set the most suitable keywords by referring to the user's past search history and improve the accuracy of filtering. Some or all of the above-described processes in the keyword filtering unit may be performed using AI, for example, or without AI. For example, the keyword filtering unit can use generative AI to refer to the user's past search history and set the most suitable keywords in order to perform keyword filtering.

[0056] The keyword filtering unit can improve the accuracy of keyword filtering by referring to the video's metadata. For example, the keyword filtering unit can identify keywords to be filtered by referring to the video's metadata. Furthermore, the keyword filtering unit can use the video's metadata to extract related keywords and improve the accuracy of filtering. In addition, the keyword filtering unit can expand the keywords to be filtered based on the video's metadata. This allows for improved filtering accuracy by referring to the video's metadata. Some or all of the above processing in the keyword filtering unit may be performed using AI, for example, or without AI. For example, the keyword filtering unit can use generative AI to refer to the video's metadata in order to perform keyword filtering and improve the accuracy of filtering.

[0057] The image recognition unit can identify inappropriate individuals using facial recognition technology when performing image recognition. For example, the image recognition unit can identify inappropriate individuals in a video using facial recognition technology. The image recognition unit can identify inappropriate individuals in a video using facial recognition technology. Furthermore, the image recognition unit can identify specific individuals in a video using facial recognition technology and filter them out. The image recognition unit can identify specific individuals in a video using facial recognition technology and filter them out. In addition, the image recognition unit can analyze the facial expressions of individuals in a video using facial recognition technology and detect inappropriate elements. This makes it easier to identify inappropriate individuals in a video by using facial recognition technology. Some or all of the above-described processes in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can perform facial recognition technology using generative AI to perform image recognition and identify inappropriate individuals.

[0058] The image recognition unit can identify inappropriate objects using object recognition technology when performing image recognition. For example, the image recognition unit can identify inappropriate objects in a video using object recognition technology. The image recognition unit can identify inappropriate objects in a video using object recognition technology. Furthermore, the image recognition unit can identify specific objects in a video and filter them using object recognition technology. The image recognition unit can identify specific objects in a video and filter them using object recognition technology. Furthermore, the image recognition unit can analyze the types of objects in a video and detect inappropriate elements using object recognition technology. The image recognition unit can analyze the types of objects in a video and detect inappropriate elements using object recognition technology. This makes it easier to identify inappropriate objects in a video by using object recognition technology. Some or all of the above processing in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can perform object recognition technology using generative AI to perform image recognition and identify inappropriate objects.

[0059] The image recognition unit can improve the efficiency of image recognition by adjusting the video's frame rate when performing image recognition. For example, the image recognition unit can improve the efficiency of image recognition by increasing the video's frame rate. The image recognition unit can also improve the efficiency of image recognition by increasing the video's frame rate. Furthermore, the image recognition unit can perform detailed image recognition by slowing down the video's frame rate. The image recognition unit can perform detailed image recognition by slowing down the video's frame rate. In addition, the image recognition unit can perform efficient image recognition by appropriately adjusting the video's frame rate. The image recognition unit can perform efficient image recognition by appropriately adjusting the video's frame rate. In this way, the efficiency of recognition can be improved by adjusting the video's frame rate. Some or all of the above processing in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can improve the efficiency of recognition by adjusting the video's frame rate using generative AI in order to perform image recognition.

[0060] The image recognition unit can improve the accuracy of image recognition by referring to the metadata of the video when performing image recognition. For example, the image recognition unit can identify the object to be recognized by referring to the metadata of the video. The image recognition unit can also improve the accuracy of recognition by using the metadata of the video to extract related objects. Furthermore, the image recognition unit can expand the objects to be recognized based on the metadata of the video. The image recognition unit can expand the objects to be recognized based on the metadata of the video. This allows for improved recognition accuracy by referring to the metadata of the video. Some or all of the above processing in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can improve the accuracy of recognition by using generative AI to refer to the metadata of the video in order to perform image recognition.

[0061] The customization unit can provide optimal settings by referring to the parent's past setting history when performing customization. For example, the customization unit can analyze the parent's past setting history and provide optimal customization settings. The customization unit can also provide customization options based on the settings that the parent frequently uses. Furthermore, the customization unit can provide highly relevant customization options from the parent's setting history. The customization unit can provide highly relevant customization options from the parent's setting history. This allows the customization unit to provide optimal customization settings by referring to the parent's past setting history. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can use generative AI to refer to the parent's past setting history and provide optimal settings in order to perform customization.

[0062] The customization unit can provide optimal settings by considering the parent's device information when performing customization. For example, if the parent is using a smartphone, the customization unit can provide customization settings that match the screen size. The customization unit can also provide customization settings optimized for larger screens if the parent is using a tablet. Furthermore, if the parent is using a smartwatch, the customization unit can provide concise and highly visible customization settings. This allows for the provision of optimal customization settings by considering the parent's device information. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can use generative AI to consider the parent's device information and provide optimal settings for customization.

[0063] The analysis unit can select the optimal analysis method by referring to past viewing history when performing analysis. For example, the analysis unit can analyze the user's past viewing history and select the optimal analysis method. The analysis unit can also prioritize analysis methods for genres that the user frequently watches. Furthermore, the analysis unit can select highly relevant analysis methods from the user's viewing history. The analysis unit can also select highly relevant analysis methods from the user's viewing history. This allows the optimal analysis method to be selected by referring to past viewing history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to refer to past viewing history and select the optimal analysis method in order to perform analysis.

[0064] The analysis unit can select the optimal analysis method by considering the user's device information when performing analysis. For example, if the user is using a smartphone, the analysis unit can provide an analysis method that matches the screen size. The analysis unit can also provide an analysis method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a smartwatch, the analysis unit can provide a concise and highly visible analysis method. This allows the analysis unit to provide the optimal analysis method by considering the user's device information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to consider the user's device information and select the optimal analysis method for performing analysis.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The automatic blocking system can also include a time management unit that manages the user's viewing time. The time management unit monitors the user's viewing time in real time and displays a warning if the set time limit is exceeded. For example, based on viewing time limits set by parents, it can automatically stop video playback if a child exceeds the allowed viewing time. The time management unit can also record viewing time history for parents to review later. Furthermore, the time management unit can analyze viewing time data and suggest appropriate viewing times. This helps to properly manage children's viewing time and promote healthy online activities.

[0067] The automated blocking system can also include a recommendation section that suggests content based on the user's viewing history. This recommendation section analyzes the user's viewing history and recommends content that might be of interest. For example, it can recommend relevant content based on the genres and themes of videos the user has previously watched. Furthermore, the recommendation section can learn the user's viewing trends and provide content best suited to each individual user. In addition, the recommendation section can collect user feedback and improve its recommendation algorithm. This allows users to easily find content that interests them, improving their viewing experience.

[0068] The automated blocking system may also include a viewing restriction unit that sets viewing restrictions based on the user's viewing history. This unit analyzes the user's viewing history and sets viewing restrictions for specific genres or themes. For example, if a user is watching an excessive amount of videos in a particular genre, the system can limit the viewing time for that genre. The viewing restriction unit can also block videos of specific genres or themes based on viewing restrictions set by parents. Furthermore, the viewing restriction unit can analyze viewing history data and suggest appropriate viewing restrictions. This allows for proper management of user viewing behavior and provides a balanced viewing experience.

[0069] The automated blocking system can also include an ad management unit that manages ad display based on the user's viewing history. This ad management unit analyzes the user's viewing history and displays highly relevant ads. For example, it can display relevant ads based on the genres and themes of videos the user has previously watched. Furthermore, the ad management unit can learn the user's viewing trends and provide ads optimized for each individual user. In addition, the ad management unit can collect user feedback and improve the ad display algorithm. This results in users being shown ads that are more likely to interest them, improving ad effectiveness.

[0070] The automated blocking system can also include an evaluation unit that evaluates content based on the user's viewing history. The evaluation unit analyzes the user's viewing history and evaluates the content they have watched. For example, it can evaluate content based on the genre and theme of the videos the user has watched. Furthermore, the evaluation unit can learn the user's viewing trends and provide the most suitable evaluation for each individual user. In addition, the evaluation unit can collect user feedback and improve its evaluation algorithm. This allows users to easily check the evaluation of the content they have watched, improving their viewing experience.

[0071] The automated blocking system can also include a filtering unit that filters content based on the user's viewing history. The filtering unit analyzes the user's viewing history and filters content for specific genres or themes. For example, if a user is excessively watching videos of a particular genre, videos of that genre can be filtered. The filtering unit can also block videos of specific genres or themes based on filtering criteria set by parents. Furthermore, the filtering unit can analyze viewing history data and suggest appropriate filtering. This allows for proper management of user viewing behavior and provides a balanced viewing experience.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The content analysis unit analyzes the content of video streaming sites in real time. Specifically, it analyzes the content of videos and detects inappropriate words and phrases. It can use natural language processing technology to analyze the text data of videos and identify inappropriate words. It can also use speech recognition technology to transcribe the audio in videos and detect inappropriate phrases. Furthermore, it can use image recognition technology to analyze the video footage and identify inappropriate scenes. Step 2: The Age Restriction Unit sets age restrictions based on the data analyzed by the Content Analysis Unit. Specifically, it sets age restrictions to display only content suitable for a particular age group. Based on age restrictions set by parents, content suitable for a specific age group can be filtered. Age restrictions can also be set based on regulations specific to each country or region. Furthermore, the criteria for age restrictions can be adjusted based on user sentiment. Step 3: The display unit shows only content suitable for a specific age group based on the age restriction set by the age restriction unit. Specifically, it displays content filtered based on age restrictions. It can also select content to display based on the user's emotions. Furthermore, it can display highly relevant content based on the user's viewing history. In addition, it can select the optimal display method based on the user's device information. Step 4: The keyword filtering unit automatically blocks content containing specific keywords or phrases. Specifically, it filters content based on keywords or phrases set by parents. Natural language processing technology can be used to analyze the meaning of keywords and improve the accuracy of filtering. Synonyms and related words can also be considered to improve filtering accuracy. Furthermore, filtering criteria can be adjusted based on the user's sentiment. Step 5: The image recognition unit identifies and blocks inappropriate images and scenes within the video. Specifically, it uses image recognition technology to identify inappropriate scenes within the video. It can also use facial recognition technology to identify inappropriate individuals. Furthermore, it can use object recognition technology to identify inappropriate objects. In addition, it can adjust the image recognition criteria based on the user's emotions.

[0074] (Example of form 2) An automated blocking system according to an embodiment of the present invention is a system for children to safely enjoy video streaming sites. This automated blocking system uses machine learning to identify inappropriate content in real time and automatically block it. Specifically, it combines content analysis, age restrictions, keyword filtering, and image recognition to provide a safety net that parents can customize according to their child's age and sensitivities. First, the automated blocking system analyzes the content of video streaming sites in real time. For example, the automated blocking system analyzes the content of videos and detects inappropriate words and phrases. Next, the automated blocking system sets age restrictions and displays only content suitable for specific age groups. Furthermore, the automated blocking system performs keyword filtering and automatically blocks content containing specific keywords or phrases. Finally, the automated blocking system uses image recognition technology to identify and block inappropriate images and scenes in videos. This automated blocking system is customizable by parents based on their child's age and sensitivities. For example, parents can set specific keywords or phrases and filter content based on them. Parents can also analyze their child's viewing history and trends to provide optimal filtering settings. This automated blocking system allows children to view safe and wholesome content, and parents can confidently manage their children's online activities. For example, if a child attempts to view inappropriate content, the automatic blocking system will automatically block it and notify the parent. This eliminates the need for parents to monitor all content, thus ensuring the safety of their children. In this way, the automatic blocking system allows children to enjoy video streaming sites safely.

[0075] The automated blocking system according to this embodiment comprises a content analysis unit, an age restriction unit, a display unit, a keyword filtering unit, and an image recognition unit. The content analysis unit analyzes the content of a video streaming site in real time. For example, the content analysis unit analyzes the content of a video and detects inappropriate words and phrases. For example, the content analysis unit can analyze the text data of a video using natural language processing technology to identify inappropriate words. The content analysis unit can also transcribe the audio in the video into text using speech recognition technology to detect inappropriate phrases. Furthermore, the content analysis unit can analyze the video footage using image recognition technology to identify inappropriate scenes. The age restriction unit sets age restrictions based on the data analyzed by the content analysis unit. For example, the age restriction unit sets age restrictions to display only content suitable for a specific age group. For example, the age restriction unit can filter content suitable for a specific age group based on age restrictions set by parents. The age restriction unit can also set age restrictions based on regulations specific to each country or region. Furthermore, the age restriction unit can adjust the age restriction criteria based on the user's feelings. The display unit shows only content suitable for a specific age group based on age restrictions set by the age restriction unit. The display unit can, for example, display content filtered based on age restrictions. The display unit can, for example, select content to display based on the user's emotions. The display unit can also display highly relevant content based on the user's viewing history. Furthermore, the display unit can select the optimal display method based on the user's device information. The keyword filtering unit automatically blocks content containing specific keywords or phrases. The keyword filtering unit filters content based on specific keywords or phrases set by parents, for example. The keyword filtering unit can, for example, analyze the meaning of keywords using natural language processing technology to improve the accuracy of filtering. The keyword filtering unit can also improve the accuracy of filtering by considering synonyms and related words.Furthermore, the keyword filtering unit can adjust the filtering criteria based on the user's emotions. The image recognition unit identifies and blocks inappropriate images and scenes in the video. For example, the image recognition unit identifies inappropriate scenes in the video using image recognition technology. For example, the image recognition unit can identify inappropriate people using facial recognition technology. The image recognition unit can also identify inappropriate objects using object recognition technology. Furthermore, the image recognition unit can adjust the image recognition criteria based on the user's emotions. As a result, the automatic blocking system according to this embodiment allows children to safely enjoy video streaming sites. Some or all of the above-described processes in the content analysis unit, age restriction unit, display unit, keyword filtering unit, and image recognition unit may be performed using AI, for example, or without AI. For example, the content analysis unit can analyze the content of a video using generative AI to analyze the content of a video streaming site in real time and detect inappropriate words and phrases. The age restriction unit can set age restrictions using generative AI and display only content suitable for a specific age group. The display unit can select content to display using generative AI and display content suitable for a specific age group. The keyword filtering unit can automatically block content containing specific keywords or phrases using generative AI. The image recognition unit can identify and block inappropriate images or scenes within a video using generative AI.

[0076] The Content Analysis Department analyzes content from video streaming sites in real time. Specifically, it uses natural language processing technology to analyze video content and detect inappropriate words and phrases. For example, it can identify inappropriate words and phrases by transcribing the audio from a video into text and analyzing that text data. By using speech recognition technology, it is possible to transcribe the audio in a video into text in real time and analyze that text data. Furthermore, the Content Analysis Department can also analyze the video content using image recognition technology to identify inappropriate scenes. For example, if a particular scene contains violent acts or inappropriate images, it can detect this and issue a warning. In this way, the Content Analysis Department can comprehensively analyze the text data, audio data, and video data of a video and detect inappropriate content in real time. Furthermore, by using generative AI, it is possible to analyze video content more advancedly and detect inappropriate words and phrases with greater accuracy. Generative AI has the ability to learn from large amounts of data and understand context and nuances, so it can identify inappropriate expressions that cannot be detected by simple keyword matching. For example, generative AI can analyze the content of a video and determine whether a particular word is inappropriate in context. This enables the content analysis unit to detect inappropriate content with greater accuracy, providing users with a safer viewing environment.

[0077] The age restriction unit sets age restrictions based on data analyzed by the content analysis unit. Specifically, the age restriction unit sets age restrictions to display only content suitable for a particular age group. For example, it can filter content suitable for a particular age group based on age restrictions set by parents. Furthermore, the age restriction unit can also set age restrictions based on country- or regional regulations. For example, if certain content is considered inappropriate for minors in a particular country, it can set age restrictions according to that country's regulations. The age restriction unit can also adjust age restriction criteria based on user sentiment. For example, if a user expresses discomfort with certain content, the age restriction criteria can be reviewed based on that feedback. Moreover, by using generative AI, the age restriction unit can set more sophisticated age restrictions. Generative AI has the ability to deeply understand the content and determine whether it is appropriate for a particular age group. For example, generative AI can analyze the content of a video and determine whether it is inappropriate for a particular age group. This allows the age restriction unit to set more accurate age restrictions and provide users with a safer viewing environment.

[0078] The display unit shows only content suitable for a specific age group based on age restrictions set by the age restriction unit. Specifically, the display unit displays content filtered based on age restrictions. For example, content deemed inappropriate based on age restrictions is not displayed, and only content deemed appropriate is shown. The display unit can also select content to display based on the user's emotions. For example, if a user shows a positive reaction to a particular piece of content, similar content can be displayed to that user. Furthermore, the display unit can also display highly relevant content based on the user's viewing history. For example, it can recommend highly relevant content based on content the user has viewed in the past. In addition, the display unit can select the optimal display method based on the user's device information. For example, it can select the optimal display method according to the device the user is using, such as a smartphone, tablet, or PC, to provide the user with a comfortable viewing experience. By using generative AI, the display unit can perform more sophisticated content selection. Generative AI has the ability to comprehensively analyze the user's viewing history, emotions, and device information to select the most suitable content. This allows the display unit to provide users with a more personalized viewing experience and improve satisfaction.

[0079] The keyword filtering unit automatically blocks content containing specific keywords or phrases. Specifically, it filters content based on specific keywords or phrases set by parents. For example, content containing certain inappropriate words or phrases is automatically blocked. The keyword filtering unit can also improve filtering accuracy by analyzing the meaning of keywords using natural language processing technology. For example, it can achieve more accurate filtering by considering synonyms and related words. Furthermore, the keyword filtering unit can adjust filtering criteria based on user sentiment. For example, if a user expresses discomfort with a particular keyword, the filtering criteria can be reviewed based on that feedback. By using generative AI, the keyword filtering unit can perform more sophisticated filtering. Generative AI has the ability to learn from large amounts of data and understand context and nuances, so it can identify inappropriate expressions that cannot be detected by simple keyword matching. For example, generative AI can analyze the content of a video and determine whether a particular keyword is inappropriate in context. As a result, the keyword filtering unit can achieve more accurate filtering of inappropriate content and provide users with a safe viewing environment.

[0080] The image recognition unit identifies and blocks inappropriate images and scenes within a video. Specifically, the image recognition unit uses image recognition technology to identify inappropriate scenes within a video. For example, if a particular scene contains violent acts or inappropriate images, it can detect them and issue a warning. The image recognition unit can identify inappropriate individuals using facial recognition technology. For example, if a particular person appears in a video, it can determine whether that person is engaging in inappropriate behavior. The image recognition unit can also identify inappropriate objects using object recognition technology. For example, if a particular object appears in a video, it can determine whether that object is inappropriate. Furthermore, the image recognition unit can adjust its image recognition criteria based on the user's emotions. For example, if a user expresses discomfort with a particular scene, the image recognition criteria can be reviewed based on that feedback. By using generative AI, the image recognition unit can perform more advanced image recognition. Generative AI has the ability to learn from large amounts of data and deeply understand scenes and objects within videos, so it can identify inappropriate scenes and objects that cannot be detected by simple image recognition. For example, generative AI can analyze the content of a video and determine whether a particular scene is contextually inappropriate. This enables the image recognition unit to identify and block inappropriate content with greater accuracy, providing users with a safer viewing environment.

[0081] The customization section allows parents to set specific keywords or phrases and filter content based on them. For example, the customization section filters content based on specific keywords or phrases set by the parents. For example, the customization section can identify content to be filtered using a list of keywords set by the parents. The customization section can also identify content to be filtered using a list of phrases set by the parents. Furthermore, the customization section can adjust the filtering criteria based on the keywords or phrases set by the parents. This allows parents to customize the filtering settings to suit their child's age and sensitivities. Some or all of the above processing in the customization section may be performed using AI, for example, or not using AI. For example, the customization section can input a list of keywords set by the parents into a generating AI and identify content to be filtered.

[0082] The analysis unit can analyze a child's viewing history and trends and provide optimal filtering settings for parents. For example, the analysis unit can analyze a child's viewing history and identify viewing trends. For example, the analysis unit can use viewing history data to analyze a child's viewing trends. The analysis unit can also provide optimal filtering settings based on viewing history data. Furthermore, the analysis unit can adjust the filtering criteria using viewing history data. This allows parents to provide optimal filtering settings based on their child's viewing history and trends. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a child's viewing history data into a generating AI and have the generating AI perform the analysis of viewing trends.

[0083] The content analysis unit can analyze the content of a video and detect inappropriate words and phrases. For example, the content analysis unit can analyze the text data of the video using natural language processing technology to identify inappropriate words. For example, the content analysis unit can analyze the content of a video using text generation AI (e.g., LLM) to detect inappropriate words and phrases. Furthermore, the content analysis unit can transcribe the audio in the video into text using speech recognition technology to detect inappropriate phrases. For example, the content analysis unit can transcribe the conversations in the video into text using speech recognition technology to identify inappropriate phrases. In addition, the content analysis unit can analyze the video footage using image recognition technology to identify inappropriate scenes. As a result, the content analysis unit can detect and block inappropriate words and phrases by analyzing the content of the video. Some or all of the above-described processes in the content analysis unit may be performed using AI, or not. For example, the content analysis unit can use generation AI to detect inappropriate words and phrases in order to analyze the content of a video.

[0084] The age restriction unit can set age restrictions to display only content suitable for a specific age group. For example, the age restriction unit can filter content suitable for a specific age group based on age restrictions set by parents. The age restriction unit can also set age restrictions based on country or region regulations. For example, the age restriction unit can refer to country-specific age restriction regulations and set appropriate age restrictions. Furthermore, the age restriction unit can adjust age restriction criteria based on user emotions. For example, if a user is experiencing stress, the age restriction unit can set stricter age restriction criteria. This ensures that children can safely view content by displaying only content suitable for a specific age group. Some or all of the above processing in the age restriction unit may be performed using AI, for example, or not. For example, the age restriction unit can use generative AI to filter content suitable for a specific age group in order to set age restrictions.

[0085] The keyword filtering unit can automatically block content containing specific keywords or phrases. For example, the keyword filtering unit filters content based on specific keywords or phrases set by parents. For example, the keyword filtering unit can identify content to be filtered using a keyword list set by parents. It can also identify content to be filtered using a phrase list set by parents. Furthermore, the keyword filtering unit can improve filtering accuracy by analyzing the meaning of keywords using natural language processing technology. For example, the keyword filtering unit can analyze the context of keywords using natural language processing technology to identify keywords with inappropriate meanings. This allows for the elimination of inappropriate content by automatically blocking content containing specific keywords or phrases. Some or all of the above-described processes in the keyword filtering unit may be performed using AI, or not. For example, the keyword filtering unit can use generative AI to automatically block content containing specific keywords or phrases in order to perform keyword filtering.

[0086] The image recognition unit can identify and block inappropriate images and scenes within a video. For example, the image recognition unit can identify inappropriate scenes within a video using image recognition technology. The image recognition unit can identify inappropriate people using facial recognition technology. For example, the image recognition unit can identify inappropriate people within a video using facial recognition technology. The image recognition unit can also identify inappropriate objects using object recognition technology. For example, the image recognition unit can identify inappropriate objects within a video using object recognition technology. Furthermore, the image recognition unit can adjust the image recognition criteria based on the user's emotions. For example, if the user is feeling stressed, the image recognition unit can set stricter image recognition criteria. This allows children to watch safely by identifying and blocking inappropriate images and scenes within the video. Some or all of the above-described processes in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can use generative AI to identify and block inappropriate images and scenes within a video in order to perform image recognition.

[0087] The content analysis unit can estimate the user's emotions and adjust the accuracy of the content analysis based on the estimated emotions. For example, if the user is stressed, the content analysis unit can increase the accuracy of the content analysis and perform stricter filtering. For example, if the user is relaxed, the content analysis unit can loosen the accuracy of the content analysis and make the filtering criteria more flexible. Also, if the user is excited, the content analysis unit can set the accuracy of the content analysis to a moderate level and perform balanced filtering. This allows for more appropriate filtering by adjusting the accuracy of the content analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the content analysis unit may be performed using AI, for example, or without AI. For example, the content analysis unit can use generative AI to estimate the user's emotions and adjust the accuracy of the content analysis based on the estimated emotions.

[0088] The content analysis unit can use speech recognition technology to detect inappropriate words and phrases when analyzing the content of a video. For example, the content analysis unit can use speech recognition technology to transcribe the audio in the video into text and detect inappropriate phrases. For example, the content analysis unit can use speech recognition technology to transcribe the conversations in the video into text and identify inappropriate phrases. The content analysis unit can also use speech recognition technology to detect inappropriate elements from the music and sound effects in the video. For example, the content analysis unit can use speech recognition technology to analyze the music and sound effects in the video and identify inappropriate elements. This makes it easier to detect inappropriate words and phrases in a video by using speech recognition technology. Some or all of the above processing in the content analysis unit may be performed using AI, for example, or without AI. For example, the content analysis unit can use generative AI to perform speech recognition technology in order to analyze the content of a video and detect inappropriate words and phrases.

[0089] The content analysis unit can analyze subtitle data to detect inappropriate words and phrases when analyzing the content of a video. For example, the content analysis unit can analyze the video's subtitle data in real time to detect inappropriate words. For example, the content analysis unit can perform text analysis on the subtitle data to identify inappropriate phrases. The content analysis unit can also use the subtitle data to analyze the conversation content within the video in detail and detect inappropriate elements. For example, the content analysis unit can use the subtitle data to analyze the conversation content within the video in detail and identify inappropriate elements. This makes it easier to detect inappropriate words and phrases within a video by analyzing the subtitle data. Some or all of the above-described processes in the content analysis unit may be performed using AI, for example, or without AI. For example, the content analysis unit can use generative AI to analyze subtitle data in order to analyze the content of a video and detect inappropriate words and phrases.

[0090] The content analysis unit can estimate the user's emotions and determine the priority of content analysis based on the estimated emotions. For example, if the user is stressed, the content analysis unit may set a high priority for content analysis. For example, if the user is relaxed, the content analysis unit may set a low priority for content analysis. Furthermore, if the user is excited, the content analysis unit may set a medium priority for content analysis. This allows for more appropriate filtering by determining the priority of content analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the content analysis unit may be performed using AI, for example, or not using AI. For example, the content analysis unit can use generative AI to estimate the user's emotions and determine the priority of content analysis based on the estimated emotions.

[0091] The content analysis unit can improve the efficiency of analysis by adjusting the video playback speed when analyzing the content of a video. For example, the content analysis unit can speed up the video playback speed to perform analysis quickly. For example, by speeding up the video playback speed, the content analysis unit can analyze a large amount of content in a short time. The content analysis unit can also slow down the video playback speed to perform detailed analysis. For example, by slowing down the video playback speed, the content analysis unit can perform detailed analysis down to the smallest detail. Furthermore, the content analysis unit can perform efficient analysis by appropriately adjusting the video playback speed. For example, by appropriately adjusting the video playback speed, the content analysis unit can perform efficient and accurate analysis. In this way, the efficiency of analysis can be improved by adjusting the video playback speed. Some or all of the above processes in the content analysis unit may be performed using AI, for example, or without using AI. For example, the content analysis unit can perform efficient analysis by adjusting the video playback speed using generative AI in order to analyze the content of a video.

[0092] The content analysis unit can improve the accuracy of its analysis by referring to the video's metadata when analyzing the video's content. For example, the content analysis unit can improve the accuracy of its analysis by referring to the video's metadata to identify the content's category. For example, the content analysis unit can improve the accuracy of its analysis by using the video's metadata to extract relevant keywords. Furthermore, the content analysis unit can improve the accuracy of its analysis by considering the video's length and publication date based on the video's metadata. For example, the content analysis unit can improve the accuracy of its analysis by considering the video's length and publication date based on the video's metadata. In this way, the accuracy of the analysis can be improved by referring to the video's metadata. Some or all of the above processes in the content analysis unit may be performed using AI, for example, or without AI. For example, the content analysis unit can improve the accuracy of its analysis by using generative AI to refer to the video's metadata in order to analyze the video's content.

[0093] The age restriction unit can estimate the user's emotions and adjust the age restriction criteria based on the estimated emotions. For example, if the user is stressed, the age restriction unit may set the age restriction criteria stricter. For example, if the user is relaxed, the age restriction unit may loosen the age restriction criteria. Also, if the user is excited, the age restriction unit may set the age restriction criteria to a moderate level. This allows for more appropriate age restrictions by adjusting the age restriction criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the age restriction unit may be performed using AI, for example, or not using AI. For example, the age restriction unit can use generative AI to estimate the user's emotions and adjust the age restriction criteria based on the estimated emotions.

[0094] The age restriction unit can set age restrictions by taking into account regulations specific to each country and region. For example, the age restriction unit can refer to age restriction regulations specific to each country and set appropriate age restrictions. The age restriction unit can also set age restrictions by taking into account the culture and customs specific to each region. Furthermore, the age restriction unit can set age restrictions by referring to international age restriction standards. The age restriction unit can set age restrictions by referring to international age restriction standards. This allows for the setting of appropriate age restrictions by taking into account regulations specific to each country and region. Some or all of the above processing in the age restriction unit may be performed using AI, for example, or not using AI. For example, the age restriction unit can use generative AI to set age restrictions by taking into account regulations specific to each country and region.

[0095] The age restriction unit can apply different criteria depending on the video genre when setting age restrictions. For example, it can apply lenient age restrictions to educational videos. It can also apply strict age restrictions to highly entertaining videos. Furthermore, it can apply moderate age restrictions to news and documentary videos. This allows for more appropriate age restrictions by applying different criteria depending on the video genre. Some or all of the above processing in the age restriction unit may be performed using AI, for example, or not. For example, the age restriction unit can use generative AI to apply different criteria depending on the video genre in order to set age restrictions.

[0096] The age restriction unit can estimate the user's emotions and adjust the display method of the age restriction based on the estimated user's emotions. For example, if the user is stressed, the age restriction unit can provide a simple and highly visible display method. The age restriction unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is excited, the age restriction unit can provide a visually stimulating display method. This allows for a more appropriate display by adjusting the age restriction display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the age restriction unit may be performed using AI, for example, or without AI. For example, the age restriction section can use generative AI to estimate the user's emotions and adjust the display method of the age restriction based on the estimated emotions.

[0097] The age restriction unit can set the optimal age restriction by referring to the user's past viewing history. For example, the age restriction unit can analyze the user's past viewing history and set an appropriate age restriction. The age restriction unit can also set age restrictions based on the user's past viewing habits. Furthermore, the age restriction unit can adjust age restrictions for specific genres based on the user's viewing history. This allows for the setting of the optimal age restriction by referring to the user's past viewing history. Some or all of the above processing in the age restriction unit may be performed using AI, for example, or without AI. For example, the age restriction unit can use generative AI to refer to the user's past viewing history and set the optimal restriction in order to set age restrictions.

[0098] The age restriction unit can customize age restrictions based on specific conditions set by parents when setting age restrictions. For example, the age restriction unit can adjust age restrictions based on specific keywords set by parents. The age restriction unit can also set age restrictions based on viewing time limits set by parents. Furthermore, the age restriction unit can customize age restrictions for specific genres set by parents. This allows for more appropriate restrictions by customizing age restrictions based on specific conditions set by parents. Some or all of the above processing in the age restriction unit may be performed using AI, for example, or not. For example, the age restriction unit can use generative AI to customize restrictions based on specific conditions set by parents in order to set age restrictions.

[0099] The display unit can estimate the user's emotions and select content to display based on the estimated emotions. For example, if the user is stressed, the display unit can prioritize displaying relaxing content. The display unit can also prioritize displaying relaxing content if the user is relaxed. Furthermore, if the user is excited, the display unit can also display educational content. This allows for the provision of more appropriate content by selecting content to display based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use generative AI to estimate the user's emotions and select content to display based on the estimated emotions.

[0100] The display unit can display the most suitable content based on the video viewing time when selecting content to display. For example, the display unit can prioritize displaying content suitable for short viewing times. The display unit can also display content suitable for long viewing times. Furthermore, the display unit can display balanced content according to viewing time. This improves the user's viewing experience by displaying the most suitable content based on the video viewing time. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use generative AI to display the most suitable content based on the video viewing time in order to select content to display.

[0101] The display unit can select content to display by referring to video ratings and comments. For example, the display unit can prioritize displaying highly-rated content. The display unit can also prioritize displaying content with many positive comments. Furthermore, the display unit can display balanced content based on ratings and comments. This allows the display unit to show content that is beneficial to the user by referring to video ratings and comments. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use a generating AI to refer to video ratings and comments in order to select content to display.

[0102] The display unit can estimate the user's emotions and adjust the order of content displayed based on the estimated emotions. For example, if the user is stressed, the display unit can display relaxing content first. The display unit can also display entertaining content first if the user is relaxed. Furthermore, if the user is excited, the display unit can display educational content first. This allows for a more appropriate viewing experience by adjusting the order of content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use generative AI to estimate the user's emotions and adjust the order of displayed content based on the estimated emotions.

[0103] The display unit can select the optimal display method by considering the user's device information when selecting content to display. For example, if the user is using a smartphone, the display unit can provide a display method that matches the screen size. The display unit can also provide a display method optimized for a larger screen if the user is using a tablet. Furthermore, if the user is using a smartwatch, the display unit can provide a concise and highly visible display method. This allows the display unit to provide the optimal display method by considering the user's device information. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use generative AI to select the optimal display method by considering the user's device information in order to select content to display.

[0104] The display unit can analyze the user's viewing history to select content to display and show highly relevant content. For example, the display unit can analyze the user's past viewing history and show highly relevant content. The display unit can also prioritize displaying content in genres that the user frequently watches. Furthermore, the display unit can display content that the user might be interested in based on their viewing history. In this way, by analyzing the user's viewing history, highly relevant content can be provided. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use generative AI to analyze the user's viewing history and display highly relevant content in order to select content to display.

[0105] The keyword filtering unit can estimate the user's emotions and adjust the filtering criteria based on the estimated emotions. For example, if the user is stressed, the keyword filtering unit can set the filtering criteria stricter. The keyword filtering unit can also loosen the filtering criteria if the user is relaxed. Furthermore, if the user is excited, the keyword filtering unit can set the filtering criteria to a moderate level. This allows for more appropriate filtering by adjusting the filtering criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the keyword filtering unit may be performed using AI, for example, or without AI. For example, the keyword filtering unit can use generative AI to estimate the user's emotions and adjust the filtering criteria based on the estimated emotions.

[0106] The keyword filtering unit can analyze the meaning of keywords using natural language processing techniques when performing keyword filtering. For example, the keyword filtering unit can analyze the context of keywords using natural language processing techniques to identify keywords with inappropriate meanings. Furthermore, the keyword filtering unit can analyze the ambiguity of keywords using natural language processing techniques to perform appropriate filtering. In addition, the keyword filtering unit can analyze the emotional nuances of keywords using natural language processing techniques to improve the accuracy of filtering. This allows for the analysis of keyword meanings and improvement of filtering accuracy by using natural language processing techniques. Some or all of the above-described processes in the keyword filtering unit may be performed using AI, for example, or without AI. For example, the keyword filtering unit can perform keyword filtering by executing natural language processing techniques using generative AI to analyze the meaning of keywords.

[0107] The keyword filtering unit can improve the accuracy of keyword filtering by considering synonyms and related words. For example, the keyword filtering unit can expand the keywords to be filtered using a synonym dictionary. Furthermore, the keyword filtering unit can also expand the keywords to be filtered using a thesaurus. The keyword filtering unit can also improve the accuracy of filtering by considering synonyms and related words. This allows for improved filtering accuracy by considering synonyms and related words. Some or all of the above processing in the keyword filtering unit may be performed using AI, for example, or without AI. For example, the keyword filtering unit can improve the accuracy of keyword filtering by considering synonyms and related words using generative AI.

[0108] The keyword filtering unit can estimate the user's emotions and determine filtering priorities based on the estimated emotions. For example, if the user is stressed, the keyword filtering unit can set a high filtering priority. The keyword filtering unit can also set a low filtering priority if the user is relaxed. Furthermore, if the user is excited, the keyword filtering unit can set a medium filtering priority. This allows for more appropriate filtering by determining filtering priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the keyword filtering unit may be performed using AI, for example, or without AI. For example, the keyword filtering unit can use generative AI to estimate the user's emotions and determine the filtering priority based on the estimated emotions.

[0109] The keyword filtering unit can set the most suitable keywords by referring to the user's past search history when performing keyword filtering. For example, the keyword filtering unit can analyze the user's past search history and set the keywords to be filtered. The keyword filtering unit can also set the keywords to be filtered based on keywords that the user frequently searches for. The keyword filtering unit can also extract highly relevant keywords from the user's search history and set them to be filtered. This allows the system to set the most suitable keywords by referring to the user's past search history and improve the accuracy of filtering. Some or all of the above-described processes in the keyword filtering unit may be performed using AI, for example, or without AI. For example, the keyword filtering unit can use generative AI to refer to the user's past search history and set the most suitable keywords in order to perform keyword filtering.

[0110] The keyword filtering unit can improve the accuracy of keyword filtering by referring to the video's metadata. For example, the keyword filtering unit can identify keywords to be filtered by referring to the video's metadata. Furthermore, the keyword filtering unit can use the video's metadata to extract related keywords and improve the accuracy of filtering. In addition, the keyword filtering unit can expand the keywords to be filtered based on the video's metadata. This allows for improved filtering accuracy by referring to the video's metadata. Some or all of the above processing in the keyword filtering unit may be performed using AI, for example, or without AI. For example, the keyword filtering unit can use generative AI to refer to the video's metadata in order to perform keyword filtering and improve the accuracy of filtering.

[0111] The image recognition unit can estimate the user's emotions and adjust the image recognition criteria based on the estimated emotions. For example, if the user is stressed, the image recognition unit can set the image recognition criteria stricter. The image recognition unit can also loosen the image recognition criteria if the user is relaxed. Furthermore, if the user is excited, the image recognition unit can set the image recognition criteria to a moderate level. This allows for more appropriate filtering by adjusting the image recognition criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit may use generative AI to estimate the user's emotions and adjust the image recognition criteria based on the estimated emotions.

[0112] The image recognition unit can identify inappropriate individuals using facial recognition technology when performing image recognition. For example, the image recognition unit can identify inappropriate individuals in a video using facial recognition technology. The image recognition unit can identify inappropriate individuals in a video using facial recognition technology. Furthermore, the image recognition unit can identify specific individuals in a video using facial recognition technology and filter them out. The image recognition unit can identify specific individuals in a video using facial recognition technology and filter them out. In addition, the image recognition unit can analyze the facial expressions of individuals in a video using facial recognition technology and detect inappropriate elements. This makes it easier to identify inappropriate individuals in a video by using facial recognition technology. Some or all of the above-described processes in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can perform facial recognition technology using generative AI to perform image recognition and identify inappropriate individuals.

[0113] The image recognition unit can identify inappropriate objects using object recognition technology when performing image recognition. For example, the image recognition unit can identify inappropriate objects in a video using object recognition technology. The image recognition unit can identify inappropriate objects in a video using object recognition technology. Furthermore, the image recognition unit can identify specific objects in a video and filter them using object recognition technology. The image recognition unit can identify specific objects in a video and filter them using object recognition technology. Furthermore, the image recognition unit can analyze the types of objects in a video and detect inappropriate elements using object recognition technology. The image recognition unit can analyze the types of objects in a video and detect inappropriate elements using object recognition technology. This makes it easier to identify inappropriate objects in a video by using object recognition technology. Some or all of the above processing in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can perform object recognition technology using generative AI to perform image recognition and identify inappropriate objects.

[0114] The image recognition unit can estimate the user's emotions and determine the priority of image recognition based on the estimated emotions. For example, if the user is feeling stressed, the image recognition unit can set a high priority for image recognition. The image recognition unit can also set a low priority for image recognition if the user is relaxed. Furthermore, if the user is excited, the image recognition unit can set a medium priority for image recognition. This allows for more appropriate filtering by determining the priority of image recognition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit may use generative AI to estimate the user's emotions and determine the priority of image recognition based on the estimated emotions.

[0115] The image recognition unit can improve the efficiency of image recognition by adjusting the video's frame rate when performing image recognition. For example, the image recognition unit can improve the efficiency of image recognition by increasing the video's frame rate. The image recognition unit can also improve the efficiency of image recognition by increasing the video's frame rate. Furthermore, the image recognition unit can perform detailed image recognition by slowing down the video's frame rate. The image recognition unit can perform detailed image recognition by slowing down the video's frame rate. In addition, the image recognition unit can perform efficient image recognition by appropriately adjusting the video's frame rate. The image recognition unit can perform efficient image recognition by appropriately adjusting the video's frame rate. In this way, the efficiency of recognition can be improved by adjusting the video's frame rate. Some or all of the above processing in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can improve the efficiency of recognition by adjusting the video's frame rate using generative AI in order to perform image recognition.

[0116] The image recognition unit can improve the accuracy of image recognition by referring to the metadata of the video when performing image recognition. For example, the image recognition unit can identify the object to be recognized by referring to the metadata of the video. The image recognition unit can also improve the accuracy of recognition by using the metadata of the video to extract related objects. Furthermore, the image recognition unit can expand the objects to be recognized based on the metadata of the video. The image recognition unit can expand the objects to be recognized based on the metadata of the video. This allows for improved recognition accuracy by referring to the metadata of the video. Some or all of the above processing in the image recognition unit may be performed using AI, for example, or without AI. For example, the image recognition unit can improve the accuracy of recognition by using generative AI to refer to the metadata of the video in order to perform image recognition.

[0117] The customization unit can estimate the user's emotions and adjust the customization settings based on the estimated emotions. For example, if the user is stressed, the customization unit can simplify the customization settings. The customization unit can also simplify the customization settings if the user is relaxed. Furthermore, if the user is excited, the customization unit can provide visually stimulating customization options. This allows for more appropriate filtering by adjusting the customization settings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization section can use generative AI to estimate the user's emotions and adjust the customization settings based on the estimated emotions.

[0118] The customization unit can provide optimal settings by referring to the parent's past setting history when performing customization. For example, the customization unit can analyze the parent's past setting history and provide optimal customization settings. The customization unit can also provide customization options based on the settings that the parent frequently uses. Furthermore, the customization unit can provide highly relevant customization options from the parent's setting history. The customization unit can provide highly relevant customization options from the parent's setting history. This allows the customization unit to provide optimal customization settings by referring to the parent's past setting history. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can use generative AI to refer to the parent's past setting history and provide optimal settings in order to perform customization.

[0119] The customization unit can estimate the user's emotions and determine the priority of customization based on the estimated user emotions. For example, if the user is feeling stressed, the customization unit can set a high priority for customization. The customization unit can also set a low priority for customization if the user is relaxed. Furthermore, if the user is excited, the customization unit can set a medium priority for customization. This allows for more appropriate filtering by determining the priority of customization based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can use generative AI to estimate the user's emotions and then determine the priority of customization based on the estimated emotions.

[0120] The customization unit can provide optimal settings by considering the parent's device information when performing customization. For example, if the parent is using a smartphone, the customization unit can provide customization settings that match the screen size. The customization unit can also provide customization settings optimized for larger screens if the parent is using a tablet. Furthermore, if the parent is using a smartwatch, the customization unit can provide concise and highly visible customization settings. This allows for the provision of optimal customization settings by considering the parent's device information. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can use generative AI to consider the parent's device information and provide optimal settings for customization.

[0121] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is stressed, the analysis unit can set the analysis criteria stricter. The analysis unit can also loosen the analysis criteria if the user is relaxed. Furthermore, if the user is excited, the analysis unit can set the analysis criteria to a moderate level. This allows for more appropriate filtering by adjusting the analysis criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to estimate the user's emotions and adjust the analysis criteria based on the estimated emotions.

[0122] The analysis unit can select the optimal analysis method by referring to past viewing history when performing analysis. For example, the analysis unit can analyze the user's past viewing history and select the optimal analysis method. The analysis unit can also prioritize analysis methods for genres that the user frequently watches. Furthermore, the analysis unit can select highly relevant analysis methods from the user's viewing history. The analysis unit can also select highly relevant analysis methods from the user's viewing history. This allows the optimal analysis method to be selected by referring to past viewing history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to refer to past viewing history and select the optimal analysis method in order to perform analysis.

[0123] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can set a high priority for the analysis. The analysis unit can also set a low priority for the analysis if the user is relaxed. Furthermore, if the user is excited, the analysis unit can set a medium priority for the analysis. This allows for more appropriate filtering by determining the priority of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to estimate the user's emotions and determine the priority of analysis based on the estimated emotions.

[0124] The analysis unit can select the optimal analysis method by considering the user's device information when performing analysis. For example, if the user is using a smartphone, the analysis unit can provide an analysis method that matches the screen size. The analysis unit can also provide an analysis method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a smartwatch, the analysis unit can provide a concise and highly visible analysis method. This allows the analysis unit to provide the optimal analysis method by considering the user's device information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use generative AI to consider the user's device information and select the optimal analysis method for performing analysis.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The automatic blocking system can also include a time management unit that manages the user's viewing time. The time management unit monitors the user's viewing time in real time and displays a warning if the set time limit is exceeded. For example, based on viewing time limits set by parents, it can automatically stop video playback if a child exceeds the allowed viewing time. The time management unit can also record viewing time history for parents to review later. Furthermore, the time management unit can analyze viewing time data and suggest appropriate viewing times. This helps to properly manage children's viewing time and promote healthy online activities.

[0127] The automated blocking system can also include a recommendation section that suggests content based on the user's viewing history. This recommendation section analyzes the user's viewing history and recommends content that might be of interest. For example, it can recommend relevant content based on the genres and themes of videos the user has previously watched. Furthermore, the recommendation section can learn the user's viewing trends and provide content best suited to each individual user. In addition, the recommendation section can collect user feedback and improve its recommendation algorithm. This allows users to easily find content that interests them, improving their viewing experience.

[0128] The automated blocking system can also include an emotion recommendation unit that estimates the user's emotions and recommends content based on those emotions. The emotion recommendation unit estimates the user's emotions in real time and recommends content appropriate to those emotions. For example, if the user is stressed, it can recommend relaxing content. If the user is relaxed, it can recommend highly entertaining content. Furthermore, if the user is excited, it can recommend educational content. This allows for a more appropriate viewing experience by providing content tailored to the user's emotions.

[0129] The automated blocking system may also include a viewing restriction unit that sets viewing restrictions based on the user's viewing history. This unit analyzes the user's viewing history and sets viewing restrictions for specific genres or themes. For example, if a user is watching an excessive amount of videos in a particular genre, the system can limit the viewing time for that genre. The viewing restriction unit can also block videos of specific genres or themes based on viewing restrictions set by parents. Furthermore, the viewing restriction unit can analyze viewing history data and suggest appropriate viewing restrictions. This allows for proper management of user viewing behavior and provides a balanced viewing experience.

[0130] The automated blocking system may further include an emotion-based viewing restriction unit that estimates the user's emotions and sets viewing restrictions based on those emotions. The emotion-based viewing restriction unit estimates the user's emotions in real time and sets viewing restrictions accordingly. For example, if the user is stressed, the viewing time can be shortened. Conversely, if the user is relaxed, the viewing time can be lengthened. Furthermore, if the user is excited, the viewing restriction can be set to a moderate level. This allows for a more appropriate viewing experience by providing viewing restrictions tailored to the user's emotions.

[0131] The automated blocking system can also include an ad management unit that manages ad display based on the user's viewing history. This ad management unit analyzes the user's viewing history and displays highly relevant ads. For example, it can display relevant ads based on the genres and themes of videos the user has previously watched. Furthermore, the ad management unit can learn the user's viewing trends and provide ads optimized for each individual user. In addition, the ad management unit can collect user feedback and improve the ad display algorithm. This results in users being shown ads that are more likely to interest them, improving ad effectiveness.

[0132] The automated blocking system can also include an emotional advertising management unit that estimates the user's emotions and manages ad display based on those emotions. This emotional advertising management unit estimates the user's emotions in real time and displays ads appropriate to those emotions. For example, if the user is stressed, it can display relaxing ads. If the user is relaxed, it can display entertaining ads. Furthermore, if the user is excited, it can display educational ads. This allows for a more appropriate advertising experience by providing ads tailored to the user's emotions.

[0133] The automated blocking system can also include an evaluation unit that evaluates content based on the user's viewing history. The evaluation unit analyzes the user's viewing history and evaluates the content they have watched. For example, it can evaluate content based on the genre and theme of the videos the user has watched. Furthermore, the evaluation unit can learn the user's viewing trends and provide the most suitable evaluation for each individual user. In addition, the evaluation unit can collect user feedback and improve its evaluation algorithm. This allows users to easily check the evaluation of the content they have watched, improving their viewing experience.

[0134] The automated blocking system may also include an emotion evaluation unit that estimates the user's emotions and evaluates the content based on those emotions. The emotion evaluation unit estimates the user's emotions in real time and provides an evaluation accordingly. For example, if the user is stressed, it can provide a harsh evaluation. If the user is relaxed, it can provide a lenient evaluation. Furthermore, if the user is excited, it can provide a moderate evaluation. This allows for a more appropriate evaluation experience by providing evaluations tailored to the user's emotions.

[0135] The automated blocking system can also include a filtering unit that filters content based on the user's viewing history. The filtering unit analyzes the user's viewing history and filters content for specific genres or themes. For example, if a user is excessively watching videos of a particular genre, videos of that genre can be filtered. The filtering unit can also block videos of specific genres or themes based on filtering criteria set by parents. Furthermore, the filtering unit can analyze viewing history data and suggest appropriate filtering. This allows for proper management of user viewing behavior and provides a balanced viewing experience.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The content analysis unit analyzes the content of video streaming sites in real time. Specifically, it analyzes the content of videos and detects inappropriate words and phrases. It can use natural language processing technology to analyze the text data of videos and identify inappropriate words. It can also use speech recognition technology to transcribe the audio in videos and detect inappropriate phrases. Furthermore, it can use image recognition technology to analyze the video footage and identify inappropriate scenes. Step 2: The Age Restriction Unit sets age restrictions based on the data analyzed by the Content Analysis Unit. Specifically, it sets age restrictions to display only content suitable for a particular age group. Based on age restrictions set by parents, content suitable for a specific age group can be filtered. Age restrictions can also be set based on regulations specific to each country or region. Furthermore, the criteria for age restrictions can be adjusted based on user sentiment. Step 3: The display unit shows only content suitable for a specific age group based on the age restriction set by the age restriction unit. Specifically, it displays content filtered based on age restrictions. It can also select content to display based on the user's emotions. Furthermore, it can display highly relevant content based on the user's viewing history. In addition, it can select the optimal display method based on the user's device information. Step 4: The keyword filtering unit automatically blocks content containing specific keywords or phrases. Specifically, it filters content based on keywords or phrases set by parents. Natural language processing technology can be used to analyze the meaning of keywords and improve the accuracy of filtering. Synonyms and related words can also be considered to improve filtering accuracy. Furthermore, filtering criteria can be adjusted based on the user's sentiment. Step 5: The image recognition unit identifies and blocks inappropriate images and scenes within the video. Specifically, it uses image recognition technology to identify inappropriate scenes within the video. It can also use facial recognition technology to identify inappropriate individuals. Furthermore, it can use object recognition technology to identify inappropriate objects. In addition, it can adjust the image recognition criteria based on the user's emotions.

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0140] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the content analysis unit, age restriction unit, display unit, keyword filtering unit, image recognition unit, customization unit, and analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the content analysis unit is implemented by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The age restriction unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The display unit is implemented by the display 40A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The keyword filtering unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The image recognition unit is implemented by the camera 42 of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The customization unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the content analysis unit, age restriction unit, display unit, keyword filtering unit, image recognition unit, customization unit, and analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the content analysis unit is implemented by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The age restriction unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The display unit is implemented by the display of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The keyword filtering unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The image recognition unit is implemented by the camera 42 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The customization unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the content analysis unit, age restriction unit, display unit, keyword filtering unit, image recognition unit, customization unit, and analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the content analysis unit is implemented by the processor 46 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The age restriction unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The display unit is implemented by the display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The keyword filtering unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The image recognition unit is implemented by the camera 42 of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The customization unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the processor 46 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0176] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0177] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0178] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0179] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0180] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0181] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0182] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0183] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0184] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0185] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0186] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0187] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0189] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0190] Each of the multiple elements described above, including the content analysis unit, age restriction unit, display unit, keyword filtering unit, image recognition unit, customization unit, and analysis unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the content analysis unit is implemented by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing unit 12. The age restriction unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The display unit is implemented by the display of the robot 414 or the specific processing unit 290 of the data processing unit 12. The keyword filtering unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The image recognition unit is implemented by the camera 42 of the robot 414 or the specific processing unit 290 of the data processing unit 12. The customization unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0191] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0192] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0193] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0194] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0195] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0196] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0197] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0198] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0199] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0200] 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.

[0201] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0202] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0203] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0204] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0205] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0206] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0207] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0208] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0209] (Note 1) The content analysis department analyzes the content of video streaming sites in real time, An age restriction unit sets age restrictions based on the data analyzed by the content analysis unit, A display unit that displays only content suitable for a specific age group based on the age restriction set by the aforementioned age restriction unit, A keyword filtering unit that automatically blocks content containing specific keywords or phrases, It includes an image recognition unit that identifies and blocks inappropriate images or scenes within the video. A system characterized by the following features. (Note 2) It includes a customization section that allows parents to set specific keywords or phrases and filter content based on them. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an analysis unit that allows parents to analyze their child's viewing history and trends and provide optimal filtering settings. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned content analysis unit, The system analyzes the video content and detects inappropriate words and phrases. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned age restriction section is, Set age restrictions to display only content suitable for a specific age group. The system described in Appendix 1, characterized by the features described herein. (Note 6) The keyword filtering unit described above is Automatically block content containing specific keywords or phrases. The system described in Appendix 1, characterized by the features described herein. (Note 7) The image recognition unit, Identify and block inappropriate images and scenes within the video. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned content analysis unit, It estimates the user's emotions and adjusts the accuracy of the content analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned content analysis unit, When analyzing the content of a video, speech recognition technology is used to detect inappropriate words and phrases. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned content analysis unit, When analyzing the content of a video, the subtitle data is analyzed to detect inappropriate words and phrases. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned content analysis unit, The system estimates user sentiment and prioritizes content analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned content analysis unit, When analyzing video content, adjust the video playback speed to improve analysis efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned content analysis unit, When analyzing video content, referencing video metadata improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned age restriction section is, The system estimates user sentiment and adjusts age restriction criteria based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned age restriction section is, When setting age restrictions, take into account regulations specific to each country and region. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned age restriction section is, When setting age restrictions, different criteria are applied depending on the video genre. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned age restriction section is, The system estimates the user's emotions and adjusts how age restrictions are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned age restriction section is, When setting age restrictions, we refer to the user's past viewing history to set the most appropriate restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned age restriction section is, When setting age restrictions, customize the restrictions based on specific conditions set by parents. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is The system estimates the user's emotions and selects content to display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When selecting content to display, the system will show the most suitable content based on the video viewing time. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is When selecting content to display, we refer to video ratings and comments. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is It estimates the user's sentiment and adjusts the order in which content is displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is When selecting content to display, the optimal display method is chosen by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is When selecting content to display, the system analyzes the user's viewing history to show highly relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The keyword filtering unit described above is It estimates the user's sentiment and adjusts the filtering criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The keyword filtering unit described above is When performing keyword filtering, natural language processing techniques are used to analyze the meaning of the keywords. The system described in Appendix 1, characterized by the features described herein. (Note 28) The keyword filtering unit described above is When performing keyword filtering, consider synonyms and related terms to improve the accuracy of the filtering. The system described in Appendix 1, characterized by the features described herein. (Note 29) The keyword filtering unit described above is It estimates the user's emotions and determines filtering priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The keyword filtering unit described above is When performing keyword filtering, the system uses the user's past search history to set the most suitable keywords. The system described in Appendix 1, characterized by the features described herein. (Note 31) The keyword filtering unit described above is When performing keyword filtering, referencing video metadata improves the accuracy of the filtering. The system described in Appendix 1, characterized by the features described herein. (Note 32) The image recognition unit, It estimates the user's emotions and adjusts the image recognition criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The image recognition unit, When performing image recognition, facial recognition technology is used to identify inappropriate individuals. The system described in Appendix 1, characterized by the features described herein. (Note 34) The image recognition unit, When performing image recognition, object recognition technology is used to identify inappropriate objects. The system described in Appendix 1, characterized by the features described herein. (Note 35) The image recognition unit, It estimates the user's emotions and determines the priority of image recognition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The image recognition unit, When performing image recognition, adjusting the video frame rate improves the efficiency of the recognition process. The system described in Appendix 1, characterized by the features described herein. (Note 37) The image recognition unit, When performing image recognition, referencing video metadata improves the accuracy of the recognition. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned customization unit is It estimates the user's emotions and adjusts customization settings based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned customization unit is When customizing settings, we refer to the parent's past settings history to provide the optimal settings. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 41) The aforementioned customization unit is When customizing, we take into account the parent's device information to provide the optimal settings. The system described in Appendix 2, characterized by the features described herein. (Note 42) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned analysis unit, When performing analysis, the optimal analysis method is selected by referring to past viewing history. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 45) The aforementioned analysis unit, When performing analysis, the optimal analysis method is selected considering the user's device information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0210] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The content analysis department analyzes the content of video streaming sites in real time, An age restriction unit sets age restrictions based on the data analyzed by the content analysis unit, A display unit that displays only content suitable for a specific age group based on the age restriction set by the aforementioned age restriction unit, A keyword filtering unit that automatically blocks content containing specific keywords or phrases, It includes an image recognition unit that identifies and blocks inappropriate images or scenes within the video. A system characterized by the following features.

2. It includes a customization section that allows parents to set specific keywords or phrases and filter content based on them. The system according to feature 1.

3. It includes an analysis unit that allows parents to analyze their child's viewing history and trends and provide optimal filtering settings. The system according to feature 1.

4. The aforementioned content analysis unit, The system analyzes the video content and detects inappropriate words and phrases. The system according to feature 1.

5. The aforementioned age restriction section is, Set age restrictions to display only content suitable for a specific age group. The system according to feature 1.

6. The keyword filtering unit described above is Automatically block content containing specific keywords or phrases. The system according to feature 1.

7. The image recognition unit, Identify and block inappropriate images and scenes within the video. The system according to feature 1.

8. The aforementioned content analysis unit, It estimates the user's emotions and adjusts the accuracy of the content analysis based on the estimated user emotions. The system according to feature 1.

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