System

Abstract

An object of a system according to an embodiment is to provide an environment in which children can safely view a video.SOLUTION: A system includes a content analysis unit, an age restriction setting unit, a keyword filtering unit, and an image recognition unit. The content analysis unit analyzes the content of the moving image. The age restriction setting part sets age restriction on the basis of the content of the moving image analyzed by the content analysis part. The keyword filtering unit blocks the moving image based on the age restriction set by the age restriction setting unit. The image recognition unit blocks the moving image based on the keyword analyzed by the keyword filtering unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology poses the risk of children accessing inappropriate content, making it difficult for parents to safely allow their children to watch videos.

[0005] The system according to the embodiment aims to provide an environment in which children can watch videos safely. [Means for solving the problem]

[0006] The system according to the embodiment includes a content analysis unit, an age restriction setting unit, a keyword filtering unit, and an image recognition unit. The content analysis unit analyzes the content of a video. The age restriction setting unit sets an age restriction based on the content of the video analyzed by the content analysis unit. The keyword filtering unit blocks videos based on the age restriction set by the age restriction setting unit. The image recognition unit blocks videos based on keywords analyzed by the keyword filtering unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an environment in which children can watch videos safely. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The automatic blocking system according to an embodiment of the present invention is a system for allowing children to enjoy YouTube safely. This system uses machine learning to identify inappropriate content in real time and automatically block it. As a result, the automatic blocking system can provide an environment in which children can enjoy YouTube safely.

[0029] An automatic blocking system according to an embodiment includes a content analysis unit, an age restriction setting unit, a keyword filtering unit, and an image recognition unit. The content analysis unit analyzes the content of a video. For example, the generation AI analyzes the content of the video and identifies inappropriate content. The generation AI detects violent scenes or inappropriate language and blocks the video. The generation AI analyzes the content based on prompts for analyzing the video content. The age restriction setting unit sets an age restriction based on the content of the video analyzed by the content analysis unit. For example, the generation AI sets an age restriction based on the content of the video. If the video contains content inappropriate for a certain age group, the generation AI sets an age restriction on the video to prevent children of that age group from viewing it. The generation AI sets the age restriction based on prompts for setting the age restriction. The keyword filtering unit blocks the video based on the age restriction set by the age restriction setting unit. For example, the generation AI analyzes keywords contained in the video title, description, comments, etc., and blocks the video if the video contains inappropriate keywords. The generation AI also detects and blocks videos containing keywords such as "violence" or "sexual." The generation AI also analyzes keywords based on prompts for keyword filtering. The image recognition unit blocks videos based on the keywords analyzed by the keyword filtering unit. For example, the generation AI analyzes images contained in video thumbnails and content and blocks videos if they contain inappropriate images. The generation AI also detects and blocks videos containing violent scenes or inappropriate images. The generation AI also analyzes images based on prompts for image recognition. As a result, the automatic blocking system according to the embodiment can provide an environment where children can safely enjoy YouTube. For example, inappropriate content is automatically blocked based on age restrictions and keyword filtering set by parents. The generation AI also analyzes video content in real time and detects inappropriate content, allowing children to watch videos safely.

[0030] The content analysis unit analyzes not only the content of the video, but also the background sounds and tone of the music, making it possible to block videos with an inappropriate atmosphere.The content analysis unit, for example, uses generative AI to analyze not only the content of the video, but also the background sounds and tone of the music.For example, if a video contains intense music or anxiety-inducing sounds that accompany violent scenes, it can detect this and block the video.This makes it possible to block videos with an inappropriate atmosphere.

[0031] The content analysis unit analyzes the facial expressions and movements of the characters in the video and can detect violent behavior and inappropriate gestures. For example, the content analysis unit uses generative AI to analyze the facial expressions of the characters in the video and detect expressions that show anger or aggression. For example, if the video contains yelling or aggressive expressions, it detects this and blocks the video. The content analysis unit also uses generative AI to analyze the movements of the characters in the video and detects violent behavior and inappropriate gestures. For example, if the video contains punching, kicking, or insulting hand movements, it detects this and blocks the video. This makes it possible to detect violent behavior and inappropriate gestures.

[0032] The content analysis unit analyzes the content of live streaming in real time and can immediately block inappropriate content. The content analysis unit uses, for example, generative AI to analyze the content of live streaming in real time and immediately block inappropriate content. For example, if violent scenes or inappropriate language are included, it can detect this and stop the live streaming. This makes it possible to immediately block inappropriate content from live streaming.

[0033] The content analysis unit can identify inappropriate content based on different cultural and regional standards and provide a blocking function customized for each region. The content analysis unit, for example, uses generative AI to analyze the content of a video and identify inappropriate content based on different cultural and regional standards. For example, if a video contains a scene that is considered inappropriate in a particular region, it detects this and blocks the video. This makes it possible to identify inappropriate content based on different cultural and regional standards and provide a blocking function customized for each region.

[0034] The age restriction setting unit can dynamically adjust the age restriction based on not only the content of the video but also the viewing history and the viewing time period. The age restriction setting unit dynamically adjusts the age restriction based on not only the content of the video but also the viewing history and the viewing time period, for example, by using a generative AI. For example, a stricter age restriction can be set for videos viewed at night. This makes it possible to dynamically adjust the age restriction based on the viewing history and the viewing time period.

[0035] When setting an age restriction based on the content of a video, the age restriction setting unit can analyze the frequency of use of language and slang in the video and set an appropriate age restriction. The age restriction setting unit can, for example, use generative AI to analyze the frequency of use of language and slang in the video and set an appropriate age restriction. For example, if inappropriate words or slang are frequently used, it can detect this and set a strict age restriction. This makes it possible to set an appropriate age restriction based on the frequency of use of language and slang.

[0036] In addition to setting an age restriction, the age restriction setting unit can set content that can only be viewed during specific time periods, thereby restricting viewing at night or during school hours. The age restriction setting unit can, for example, use a generation AI to set content that can only be viewed during specific time periods, in addition to setting an age restriction. For example, it can prevent viewing at night or during school hours. This makes it possible to set content that can only be viewed during specific time periods, thereby restricting viewing at night or during school hours.

[0037] The age restriction setting unit can add a function to recommend appropriate content based on specific educational goals set by parents when setting an age restriction. The age restriction setting unit, for example, uses a generative AI to recommend appropriate content based on specific educational goals set by parents when setting an age restriction. For example, videos that are useful for learning may be preferentially displayed. This makes it possible to recommend appropriate content based on the educational goals set by parents.

[0038] The keyword filtering unit can perform a broader keyword analysis by taking synonyms and similar words into account when filtering keywords. The keyword filtering unit can perform a broader keyword analysis by taking synonyms and similar words into account when filtering keywords, for example, using generative AI. For example, if a video contains a synonym or similar word for "violence," it can detect this and block the video. This allows for a broader keyword analysis by taking synonyms and similar words into account.

[0039] In addition to keyword filtering, the keyword filtering unit can also analyze video tags and metadata to identify inappropriate content. In addition to keyword filtering, the keyword filtering unit can also analyze video tags and metadata to identify inappropriate content, for example, using generative AI. For example, if a video contains tags such as "violence" or "sexual," it will detect this and block the video. This makes it possible to analyze video tags and metadata to identify inappropriate content.

[0040] The keyword filtering unit provides an interface that allows parents to easily customize the keyword filtering settings, and can enable them to manually add and delete specific keywords.The keyword filtering unit provides an interface that allows parents to easily customize the keyword filtering settings, for example, by using a generative AI.For example, it can enable them to manually add and delete specific keywords.This provides an interface that parents can easily customize, and can enable them to manually add and delete specific keywords.

[0041] The image recognition unit can analyze images not only in the video thumbnail but also in each frame of the video to detect inappropriate scenes.The image recognition unit can, for example, use generative AI to analyze images not only in the video thumbnail but also in each frame of the video to detect inappropriate scenes.For example, if a video contains violent scenes or inappropriate images, it will detect this and block the video.This makes it possible to analyze images in each frame of the video to detect inappropriate scenes.

[0042] The image recognition unit also analyzes the text and subtitles in the video when recognizing images, and can block the video if it contains inappropriate content. The image recognition unit, for example, uses generative AI to analyze the text and subtitles in the video when recognizing images, and can block the video if it contains inappropriate content. For example, if text or subtitles such as "violence" or "sexual" are included, they are detected and the video is blocked. This makes it possible to analyze the text and subtitles in the video and block the video if it contains inappropriate content.

[0043] In addition to image recognition, the image recognition unit also analyzes the audio and music in the video, and can block the video if it contains inappropriate content.The image recognition unit, for example, uses generative AI to analyze the audio and music in the video in addition to image recognition, and can block the video if it contains inappropriate content.For example, if violent language or inappropriate music is included, it detects this and blocks the video.This makes it possible to analyze the audio and music in the video and block the video if it contains inappropriate content.

[0044] The image recognition unit provides an interface that allows parents to easily customize the image recognition settings, and can enable them to manually add and delete specific images and scenes.The image recognition unit provides an interface that allows parents to easily customize the image recognition settings, for example, by using generative AI.For example, it can enable them to manually add and delete specific images and scenes.This provides an interface that parents can easily customize, and can enable them to manually add and delete specific images and scenes.

[0045] The customizable safety net unit can analyze a child's viewing history and viewing time periods based on the customization set by the parent or guardian, and dynamically adjust the safety net. The customizable safety net unit can, for example, use a generation AI to analyze a child's viewing history and viewing time periods based on the customization set by the parent or guardian, and dynamically adjust the safety net. For example, strict restrictions can be set on videos viewed at night. This makes it possible to analyze a child's viewing history and viewing time periods based on the customization set by the parent or guardian, and dynamically adjust the safety net.

[0046] The customizable safety net section adds a feature to the customizable safety net settings that allows parents to set specific educational goals, and is able to recommend appropriate content based on those goals. For example, the customizable safety net section uses generative AI to add a feature to the customizable safety net settings that allows parents to set specific educational goals, and is able to recommend appropriate content based on those goals. For example, videos that are useful for learning may be preferentially displayed. This allows parents to set specific educational goals, and is able to recommend appropriate content based on those goals.

[0047] In addition to the customizable safety net, the customizable safety net unit can provide a dashboard that allows parents to monitor their children's viewing history and viewing time in real time. In addition to the customizable safety net, the customizable safety net unit can, for example, use a generative AI to provide a dashboard that allows parents to monitor their children's viewing history and viewing time in real time. For example, the viewing history and viewing time can be displayed in graphs. This makes it possible to provide a dashboard that allows parents to monitor their children's viewing history and viewing time in real time.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The automatic blocking system can further include a history analysis unit that analyzes a user's viewing history. The history analysis unit analyzes the trends of videos the user has viewed in the past and detects specific patterns. For example, it can identify the genres and themes that the user frequently watches and predict and block inappropriate content based on that. The history analysis unit can also analyze ratings and comments on videos the user has viewed in the past to understand the user's preferences and interests. This allows for a more accurate blocking function based on the user's viewing history.

[0050] The automatic blocking system may further include an environment analysis unit that analyzes the user's viewing environment. The environment analysis unit analyzes the surrounding environment when the user is watching a video and blocks inappropriate content. For example, if the user is watching a video in a public place, the environment analysis unit may automatically mute the audio or display subtitles. The environment analysis unit may also provide appropriate content based on the type of device and screen size used by the user. This allows for flexible blocking functionality tailored to the user's viewing environment.

[0051] The automatic blocking system can further include a time management unit that manages a user's viewing time. The time management unit analyzes the time of day and viewing time of a user to block inappropriate content. For example, it can set strict restrictions on videos viewed at night or during school hours. The time management unit can also display a message encouraging a user to take a break if the user watches videos for a long period of time. This allows the system to manage a user's viewing time and promote healthy viewing habits.

[0052] The automatic blocking system may further include an educational recommendation unit that recommends educational content based on the user's viewing history. The educational recommendation unit analyzes the content and ratings of videos the user has previously viewed and prioritizes the display of content with high educational value. For example, if a user frequently watches videos that are useful for learning, videos in that genre or theme may be displayed with priority. The educational recommendation unit may also recommend appropriate content based on educational goals set by parents. This supports the user's learning and provides content with high educational value.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The content analysis unit analyzes the content of the video. For example, the generation AI analyzes the content of the video and identifies inappropriate content. The generation AI also detects if the video contains violent scenes or inappropriate language and blocks the video. The generation AI also analyzes the content based on prompts to analyze the video content. Step 2: The age restriction setting unit sets an age restriction based on the content of the video analyzed by the content analysis unit. For example, the generation AI sets an age restriction based on the content of the video. If the video contains content that is not suitable for a certain age group, the generation AI sets an age restriction on the video to prevent children of that age group from viewing it. The generation AI also sets an age restriction based on a prompt for setting an age restriction. Step 3: The keyword filtering unit blocks videos based on the age restrictions set by the age restriction setting unit. For example, the generation AI analyzes keywords contained in the video title, description, comments, etc., and blocks the video if it contains inappropriate keywords. The generation AI also detects keywords such as "violence" or "sexual" and blocks the video. The generation AI also analyzes keywords based on prompts for keyword filtering. Step 4: The image recognition unit blocks videos based on the keywords analyzed by the keyword filtering unit. For example, the generation AI analyzes the images in the video thumbnails and content, and blocks the video if it contains inappropriate images. The generation AI also detects violent scenes or inappropriate images and blocks the video. The generation AI also analyzes images based on prompts for image recognition.

[0055] (Example 2) The automatic blocking system according to an embodiment of the present invention is a system for allowing children to enjoy YouTube safely. This system uses machine learning to identify inappropriate content in real time and automatically block it. As a result, the automatic blocking system can provide an environment in which children can enjoy YouTube safely.

[0056] An automatic blocking system according to an embodiment includes a content analysis unit, an age restriction setting unit, a keyword filtering unit, and an image recognition unit. The content analysis unit analyzes the content of a video. For example, the generation AI analyzes the content of the video and identifies inappropriate content. The generation AI detects violent scenes or inappropriate language and blocks the video. The generation AI analyzes the content based on prompts for analyzing the video content. The age restriction setting unit sets an age restriction based on the content of the video analyzed by the content analysis unit. For example, the generation AI sets an age restriction based on the content of the video. If the video contains content inappropriate for a certain age group, the generation AI sets an age restriction on the video to prevent children of that age group from viewing it. The generation AI sets the age restriction based on prompts for setting the age restriction. The keyword filtering unit blocks the video based on the age restriction set by the age restriction setting unit. For example, the generation AI analyzes keywords contained in the video title, description, comments, etc., and blocks the video if the video contains inappropriate keywords. The generation AI also detects and blocks videos containing keywords such as "violence" or "sexual." The generation AI also analyzes keywords based on prompts for keyword filtering. The image recognition unit blocks videos based on the keywords analyzed by the keyword filtering unit. For example, the generation AI analyzes images contained in video thumbnails and content and blocks videos if they contain inappropriate images. The generation AI also detects and blocks videos containing violent scenes or inappropriate images. The generation AI also analyzes images based on prompts for image recognition. As a result, the automatic blocking system according to the embodiment can provide an environment where children can safely enjoy YouTube. For example, inappropriate content is automatically blocked based on age restrictions and keyword filtering set by parents. The generation AI also analyzes video content in real time and detects inappropriate content, allowing children to watch videos safely.

[0057] The content analysis unit analyzes not only the content of the video, but also the background sounds and tone of the music, making it possible to block videos with an inappropriate atmosphere.The content analysis unit, for example, uses generative AI to analyze not only the content of the video, but also the background sounds and tone of the music.For example, if a video contains intense music or anxiety-inducing sounds that accompany violent scenes, it can detect this and block the video.This makes it possible to block videos with an inappropriate atmosphere.

[0058] The content analysis unit analyzes the facial expressions and movements of the characters in the video and can detect violent behavior and inappropriate gestures. For example, the content analysis unit uses generative AI to analyze the facial expressions of the characters in the video and detect expressions that show anger or aggression. For example, if the video contains yelling or aggressive expressions, it detects this and blocks the video. The content analysis unit also uses generative AI to analyze the movements of the characters in the video and detects violent behavior and inappropriate gestures. For example, if the video contains punching, kicking, or insulting hand movements, it detects this and blocks the video. This makes it possible to detect violent behavior and inappropriate gestures.

[0059] The content analysis unit uses emotion estimation to evaluate the emotional impact of video content on viewers and can block videos that evoke negative emotions. The content analysis unit, for example, uses generative AI to evaluate the emotional impact of video content on viewers. For example, if a video contains scenes that evoke fear or anxiety, it can detect this and block the video. This makes it possible to block videos that may evoke negative emotions.

[0060] The content analysis unit analyzes the content of live streaming in real time and can immediately block inappropriate content. The content analysis unit uses, for example, generative AI to analyze the content of live streaming in real time and immediately block inappropriate content. For example, if violent scenes or inappropriate language are included, it can detect this and stop the live streaming. This makes it possible to immediately block inappropriate content from live streaming.

[0061] The content analysis unit can identify inappropriate content based on different cultural and regional standards and provide a blocking function customized for each region. The content analysis unit, for example, uses generative AI to analyze the content of a video and identify inappropriate content based on different cultural and regional standards. For example, if a video contains a scene that is considered inappropriate in a particular region, it detects this and blocks the video. This makes it possible to identify inappropriate content based on different cultural and regional standards and provide a blocking function customized for each region.

[0062] The content analysis unit uses an emotion estimation function to monitor in real time the emotional impact that video content has on viewers, and can prioritize the display of videos that elicit positive emotions. The content analysis unit, for example, uses generative AI to monitor in real time the emotional impact that video content has on viewers, and can prioritize the display of videos that elicit positive emotions. For example, if a video contains a scene that evokes joy or surprise, it can detect this and display the video preferentially. This makes it possible to prioritize the display of videos that elicit positive emotions.

[0063] The age restriction setting unit can dynamically adjust the age restriction based on not only the content of the video but also the viewing history and the viewing time period. The age restriction setting unit dynamically adjusts the age restriction based on not only the content of the video but also the viewing history and the viewing time period, for example, by using a generative AI. For example, a stricter age restriction can be set for videos viewed at night. This makes it possible to dynamically adjust the age restriction based on the viewing history and the viewing time period.

[0064] When setting an age restriction based on the content of a video, the age restriction setting unit can analyze the frequency of use of language and slang in the video and set an appropriate age restriction. The age restriction setting unit can, for example, use generative AI to analyze the frequency of use of language and slang in the video and set an appropriate age restriction. For example, if inappropriate words or slang are frequently used, it can detect this and set a strict age restriction. This makes it possible to set an appropriate age restriction based on the frequency of use of language and slang.

[0065] The age restriction setting unit can use an emotion estimation function to evaluate the emotional impact of video content on a specific age group and display only videos that are emotionally safe. The age restriction setting unit can use, for example, generative AI to evaluate the emotional impact of video content on a specific age group and display only videos that are emotionally safe. For example, if a video contains scenes that cause fear or anxiety, it can detect this and block the video. This makes it possible to display only videos that are emotionally safe for a specific age group.

[0066] In addition to setting an age restriction, the age restriction setting unit can set content that can only be viewed during specific time periods, thereby restricting viewing at night or during school hours. The age restriction setting unit can, for example, use a generation AI to set content that can only be viewed during specific time periods, in addition to setting an age restriction. For example, it can prevent viewing at night or during school hours. This makes it possible to set content that can only be viewed during specific time periods, thereby restricting viewing at night or during school hours.

[0067] The age restriction setting unit can add a function to recommend appropriate content based on specific educational goals set by parents when setting an age restriction. The age restriction setting unit, for example, uses a generative AI to recommend appropriate content based on specific educational goals set by parents when setting an age restriction. For example, videos that are useful for learning may be preferentially displayed. This makes it possible to recommend appropriate content based on the educational goals set by parents.

[0068] The age restriction setting unit can use the emotion estimation function to identify content that a specific age group is most interested in and display that content preferentially. The age restriction setting unit can use, for example, a generation AI to identify content that a specific age group is most interested in using the emotion estimation function and display that content preferentially. For example, videos that are popular with a specific age group are displayed preferentially. This makes it possible to display content that a specific age group is most interested in preferentially.

[0069] The keyword filtering unit can perform a broader keyword analysis by taking synonyms and similar words into account when filtering keywords. The keyword filtering unit can perform a broader keyword analysis by taking synonyms and similar words into account when filtering keywords, for example, using generative AI. For example, if a video contains a synonym or similar word for "violence," it can detect this and block the video. This allows for a broader keyword analysis by taking synonyms and similar words into account.

[0070] The keyword filtering unit uses an emotion estimation function to evaluate the emotional impact of keywords on viewers and can block keywords that may evoke negative emotions. The keyword filtering unit uses, for example, generative AI to evaluate the emotional impact of keywords on viewers and can block keywords that may evoke negative emotions. For example, if a video contains keywords that evoke "fear" or "anxiety," the unit detects this and blocks the video. This makes it possible to block keywords that may evoke negative emotions.

[0071] In addition to keyword filtering, the keyword filtering unit can also analyze video tags and metadata to identify inappropriate content. In addition to keyword filtering, the keyword filtering unit can also analyze video tags and metadata to identify inappropriate content, for example, using generative AI. For example, if a video contains tags such as "violence" or "sexual," it will detect this and block the video. This makes it possible to analyze video tags and metadata to identify inappropriate content.

[0072] The keyword filtering unit provides an interface that allows parents to easily customize the keyword filtering settings, and can enable them to manually add and delete specific keywords.The keyword filtering unit provides an interface that allows parents to easily customize the keyword filtering settings, for example, by using a generative AI.For example, it can enable them to manually add and delete specific keywords.This provides an interface that parents can easily customize, and can enable them to manually add and delete specific keywords.

[0073] The keyword filtering unit uses an emotion estimation function to monitor in real time the emotional impact that specific keywords have on viewers, and can prioritize displaying keywords that elicit positive emotions. The keyword filtering unit, for example, uses generative AI to monitor in real time the emotional impact that specific keywords have on viewers, and can prioritize displaying keywords that elicit positive emotions. For example, if keywords that evoke "joy" or "surprise" are included, they are detected and displayed preferentially. This makes it possible to prioritize displaying keywords that elicit positive emotions.

[0074] The image recognition unit can analyze images not only in the video thumbnail but also in each frame of the video to detect inappropriate scenes.The image recognition unit can, for example, use generative AI to analyze images not only in the video thumbnail but also in each frame of the video to detect inappropriate scenes.For example, if a video contains violent scenes or inappropriate images, it will detect this and block the video.This makes it possible to analyze images in each frame of the video to detect inappropriate scenes.

[0075] The image recognition unit also analyzes the text and subtitles in the video when recognizing images, and can block the video if it contains inappropriate content. The image recognition unit, for example, uses generative AI to analyze the text and subtitles in the video when recognizing images, and can block the video if it contains inappropriate content. For example, if text or subtitles such as "violence" or "sexual" are included, they are detected and the video is blocked. This makes it possible to analyze the text and subtitles in the video and block the video if it contains inappropriate content.

[0076] The image recognition unit uses an emotion estimation function to evaluate the emotional impact of an image on a viewer and can block images that may evoke negative emotions. The image recognition unit uses, for example, generative AI to evaluate the emotional impact of an image on a viewer using the emotion estimation function and blocks images that may evoke negative emotions. For example, if an image that evokes fear or anxiety is included, it detects this and blocks the video. This makes it possible to block images that may evoke negative emotions.

[0077] In addition to image recognition, the image recognition unit also analyzes the audio and music in the video, and can block the video if it contains inappropriate content.The image recognition unit, for example, uses generative AI to analyze the audio and music in the video in addition to image recognition, and can block the video if it contains inappropriate content.For example, if violent language or inappropriate music is included, it detects this and blocks the video.This makes it possible to analyze the audio and music in the video and block the video if it contains inappropriate content.

[0078] The image recognition unit provides an interface that allows parents to easily customize the image recognition settings, and can enable them to manually add and delete specific images and scenes.The image recognition unit provides an interface that allows parents to easily customize the image recognition settings, for example, by using generative AI.For example, it can enable them to manually add and delete specific images and scenes.This provides an interface that parents can easily customize, and can enable them to manually add and delete specific images and scenes.

[0079] The image recognition unit uses an emotion estimation function to monitor in real time the emotional impact that specific images have on viewers, and can prioritize the display of images that elicit positive emotions. The image recognition unit, for example, uses generative AI to monitor in real time the emotional impact that specific images have on viewers, and can prioritize the display of images that elicit positive emotions. For example, if the image contains images that evoke "joy" or "surprise," it detects these and displays them preferentially. This makes it possible to prioritize the display of images that elicit positive emotions.

[0080] The customizable safety net unit can analyze a child's viewing history and viewing time periods based on the customization set by the parent or guardian, and dynamically adjust the safety net. The customizable safety net unit can, for example, use a generation AI to analyze a child's viewing history and viewing time periods based on the customization set by the parent or guardian, and dynamically adjust the safety net. For example, strict restrictions can be set on videos viewed at night. This makes it possible to analyze a child's viewing history and viewing time periods based on the customization set by the parent or guardian, and dynamically adjust the safety net.

[0081] The customizable safety net section adds a feature to the customizable safety net settings that allows parents to set specific educational goals, and is able to recommend appropriate content based on those goals. For example, the customizable safety net section uses generative AI to add a feature to the customizable safety net settings that allows parents to set specific educational goals, and is able to recommend appropriate content based on those goals. For example, videos that are useful for learning may be preferentially displayed. This allows parents to set specific educational goals, and is able to recommend appropriate content based on those goals.

[0082] The customizable safety net unit can use an emotion estimation function to preferentially display emotionally safe content based on a child's viewing history. The customizable safety net unit can, for example, use a generative AI to preferentially display emotionally safe content based on a child's viewing history using the emotion estimation function. For example, videos that do not cause fear or anxiety are preferentially displayed. This makes it possible to preferentially display emotionally safe content based on a child's viewing history.

[0083] In addition to the customizable safety net, the customizable safety net unit can provide a dashboard that allows parents to monitor their children's viewing history and viewing time in real time. In addition to the customizable safety net, the customizable safety net unit can, for example, use a generative AI to provide a dashboard that allows parents to monitor their children's viewing history and viewing time in real time. For example, the viewing history and viewing time can be displayed in graphs. This makes it possible to provide a dashboard that allows parents to monitor their children's viewing history and viewing time in real time.

[0084] The customizable safety net unit can use an emotion estimation function to identify content that children are most interested in and prioritize displaying that content. The customizable safety net unit can, for example, use generative AI to identify content that children are most interested in using the emotion estimation function and prioritize displaying that content. For example, if a child is interested in a particular theme or character, it can detect that and prioritize displaying it. This allows the content that children are most interested in to be displayed preferentially.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The automatic blocking system can further include a history analysis unit that analyzes a user's viewing history. The history analysis unit analyzes the trends of videos the user has viewed in the past and detects specific patterns. For example, it can identify the genres and themes that the user frequently watches and predict and block inappropriate content based on that. The history analysis unit can also analyze ratings and comments on videos the user has viewed in the past to understand the user's preferences and interests. This allows for a more accurate blocking function based on the user's viewing history.

[0087] The automatic blocking system may further include an environment analysis unit that analyzes the user's viewing environment. The environment analysis unit analyzes the surrounding environment when the user is watching a video and blocks inappropriate content. For example, if the user is watching a video in a public place, the environment analysis unit may automatically mute the audio or display subtitles. The environment analysis unit may also provide appropriate content based on the type of device and screen size used by the user. This allows for flexible blocking functionality tailored to the user's viewing environment.

[0088] The automatic blocking system can further include a time management unit that manages a user's viewing time. The time management unit analyzes the time of day and viewing time of a user to block inappropriate content. For example, it can set strict restrictions on videos viewed at night or during school hours. The time management unit can also display a message encouraging a user to take a break if the user watches videos for a long period of time. This allows the system to manage a user's viewing time and promote healthy viewing habits.

[0089] The automatic blocking system can further include an emotion evaluation unit that evaluates the emotional impact of a video based on the user's viewing history. The emotion evaluation unit analyzes the content of videos the user has previously viewed and their reactions after viewing, and prioritizes the display of emotionally safe content. For example, if a user has previously viewed a video with a positive reaction, videos of that genre or theme can be displayed preferentially. The emotion evaluation unit can also monitor the user's emotional reactions while viewing in real time and block videos that may evoke negative emotions. This ensures the user's emotional safety and provides a positive viewing experience.

[0090] The automatic blocking system may further include an educational recommendation unit that recommends educational content based on the user's viewing history. The educational recommendation unit analyzes the content and ratings of videos the user has previously viewed and prioritizes the display of content with high educational value. For example, if a user frequently watches videos that are useful for learning, videos in that genre or theme may be displayed with priority. The educational recommendation unit may also recommend appropriate content based on educational goals set by parents. This supports the user's learning and provides content with high educational value.

[0091] The automatic blocking system can further include an emotion evaluation unit that evaluates the emotional impact of a video based on the user's viewing history. The emotion evaluation unit analyzes the content of videos the user has previously viewed and their reactions after viewing, and prioritizes the display of emotionally safe content. For example, if a user has previously viewed a video with a positive reaction, videos of that genre or theme can be displayed preferentially. The emotion evaluation unit can also monitor the user's emotional reactions while viewing in real time and block videos that may evoke negative emotions. This ensures the user's emotional safety and provides a positive viewing experience.

[0092] The automatic blocking system can further include an emotion evaluation unit that evaluates the emotional impact of a video based on the user's viewing history. The emotion evaluation unit analyzes the content of videos the user has previously viewed and their reactions after viewing, and prioritizes the display of emotionally safe content. For example, if a user has previously viewed a video with a positive reaction, videos of that genre or theme can be displayed preferentially. The emotion evaluation unit can also monitor the user's emotional reactions while viewing in real time and block videos that may evoke negative emotions. This ensures the user's emotional safety and provides a positive viewing experience.

[0093] The automatic blocking system can further include an emotion evaluation unit that evaluates the emotional impact of a video based on the user's viewing history. The emotion evaluation unit analyzes the content of videos the user has previously viewed and their reactions after viewing, and prioritizes the display of emotionally safe content. For example, if a user has previously viewed a video with a positive reaction, videos of that genre or theme can be displayed preferentially. The emotion evaluation unit can also monitor the user's emotional reactions while viewing in real time and block videos that may evoke negative emotions. This ensures the user's emotional safety and provides a positive viewing experience.

[0094] The automatic blocking system can further include an emotion evaluation unit that evaluates the emotional impact of a video based on the user's viewing history. The emotion evaluation unit analyzes the content of videos the user has previously viewed and their reactions after viewing, and prioritizes the display of emotionally safe content. For example, if a user has previously viewed a video with a positive reaction, videos of that genre or theme can be displayed preferentially. The emotion evaluation unit can also monitor the user's emotional reactions while viewing in real time and block videos that may evoke negative emotions. This ensures the user's emotional safety and provides a positive viewing experience.

[0095] The automatic blocking system can further include an emotion evaluation unit that evaluates the emotional impact of a video based on the user's viewing history. The emotion evaluation unit analyzes the content of videos the user has previously viewed and their reactions after viewing, and prioritizes the display of emotionally safe content. For example, if a user has previously viewed a video with a positive reaction, videos of that genre or theme can be displayed preferentially. The emotion evaluation unit can also monitor the user's emotional reactions while viewing in real time and block videos that may evoke negative emotions. This ensures the user's emotional safety and provides a positive viewing experience.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The content analysis unit analyzes the content of the video. For example, the generation AI analyzes the content of the video and identifies inappropriate content. The generation AI also detects if the video contains violent scenes or inappropriate language and blocks the video. The generation AI also analyzes the content based on prompts to analyze the video content. Step 2: The age restriction setting unit sets an age restriction based on the content of the video analyzed by the content analysis unit. For example, the generation AI sets an age restriction based on the content of the video. If the video contains content that is not suitable for a certain age group, the generation AI sets an age restriction on the video to prevent children of that age group from viewing it. The generation AI also sets an age restriction based on a prompt for setting an age restriction. Step 3: The keyword filtering unit blocks videos based on the age restrictions set by the age restriction setting unit. For example, the generation AI analyzes keywords contained in the video title, description, comments, etc., and blocks the video if it contains inappropriate keywords. The generation AI also detects keywords such as "violence" or "sexual" and blocks the video. The generation AI also analyzes keywords based on prompts for keyword filtering. Step 4: The image recognition unit blocks videos based on the keywords analyzed by the keyword filtering unit. For example, the generation AI analyzes the images in the video thumbnails and content, and blocks the video if it contains inappropriate images. The generation AI also detects violent scenes or inappropriate images and blocks the video. The generation AI also analyzes images based on prompts for image recognition.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0142] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0149] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0150] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0155] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0156] 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.

[0157] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0160] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0162] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a content analysis unit that analyzes the content of the video; an age restriction setting unit that sets an age restriction based on the content of the video analyzed by the content analysis unit; a keyword filtering unit that blocks videos based on the age restriction set by the age restriction setting unit; an image recognition unit that blocks videos based on the keywords analyzed by the keyword filtering unit. A system characterized by:

2. The content analysis unit Analyze live streaming content in real time and instantly block inappropriate content 2. The system of claim 1.

3. The age restriction setting unit Dynamically adjust age restrictions based on viewing history and time of day, as well as the content of the video.

2. The system of claim 1.

4. The keyword filtering unit Analyzes the title and description of the video, as well as the content of the comment section, and blocks the video if it contains inappropriate keywords.

2. The system of claim 1.

5. The image recognition unit Analyzes not only thumbnails of the video but also each frame of the video to detect inappropriate scenes 2. The system of claim 1.

6. The content analysis unit Evaluating the emotional impact of the content of said videos on viewers and blocking videos that evoke negative emotions 2. The system of claim 1.

Images