System

Abstract

An object of a system according to an embodiment is to automatically adjust the color and font of media content and extract important points.SOLUTION: A system according to an embodiment includes a generation AI, a tint adjustment unit, a font adjustment unit, and a keypoint extraction unit. The generation AI automatically adjusts the tint of the media content. The tint adjustment unit automatically adjusts the tint of the media content. The font adjustment unit automatically adjusts the font based on the tint adjusted by the tint adjustment unit. The key point extractor extracts an important point based on the font adjusted by the font adjuster.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 technologies require manual adjustment of color and font of media content, resulting in high costs and low efficiency.

[0005] The system according to the embodiment aims to automatically adjust the color and font of media content and extract important points. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a color adjustment unit, a font adjustment unit, and a key point extraction unit. The generation AI automatically adjusts the color of media content. The color adjustment unit automatically adjusts the color of media content. The font adjustment unit automatically adjusts the font based on the color adjusted by the color adjustment unit. The key point extraction unit extracts important points based on the font adjusted by the font adjustment unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically adjust the color and font of media content and extract important points. [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 media content adjustment system according to an embodiment of the present invention uses generative AI to automatically adjust fine details such as color and font of media content, thereby accelerating cost reduction and media globalization, and enabling viewers to enjoy the content without missing important points regardless of language.

[0029] A media content adjustment system according to an embodiment includes a generation AI, a color adjustment unit, a font adjustment unit, and a key point extraction unit. The generation AI automatically adjusts the color and font of media content. The color adjustment unit, for example, selects optimal colors according to video scenes to provide visually appealing content. The color adjustment unit can also use, for example, viewer gaze tracking data to adjust colors in real time according to the level of gaze concentration. The color adjustment unit can also automatically adjust colors to suit different devices (smartphones, tablets, PCs, etc.). The font adjustment unit, for example, provides subtitles and text that are easy for viewers to read. The font adjustment unit can also analyze, for example, viewers' past viewing history to personalize fonts that are optimal for each individual viewer. The font adjustment unit can also improve accessibility by adjusting fonts specifically for visually impaired people. The key point extraction unit, for example, automatically detects important lines and scenes in a video and adds subtitles and highlights to prevent viewers from missing them. The key point extraction unit can also highlight important points according to the viewer's emotional state, for example. The key point extraction unit can also highlight important points when the viewer's gaze shifts, for example, by using viewer gaze tracking data. As a result, the media content adjustment system according to the embodiment automatically adjusts the color and font of media content and extracts important points, allowing viewers to grasp important information regardless of language.

[0030] The color adjustment unit can select the optimal color according to the scene in the video and provide visually appealing content. For example, the color adjustment unit can select the optimal color according to the scene in the video and provide visually appealing content. For example, the generation AI can collect viewer gaze tracking data in real time and emphasize the color of the part where the gaze is focused. The color adjustment unit can also use the viewer gaze tracking data to adjust the color in real time according to the degree of gaze concentration. For example, if the gaze is focused on specific text, the color of that text can be brightened. The color adjustment unit can also automatically adjust the optimal color for different devices (smartphones, tablets, PCs, etc.). For example, it can detect the screen size and resolution of the device and optimize the color accordingly. This allows the optimal color to be selected according to the scene in the video, providing visually appealing content.

[0031] The font adjustment unit can provide subtitles and text that are easy for viewers to read. For example, the generation AI can analyze a viewer's past viewing history to identify the font that the viewer prefers. The font adjustment unit can also analyze a viewer's past viewing history to personalize the optimal font for each viewer. For example, it can extract font patterns that the viewer prefers from the viewing history and adjust the font based on that. The font adjustment unit can also improve accessibility by adjusting fonts specifically for the visually impaired. For example, it can automatically select a high-contrast font for the visually impaired. This improves the viewing experience by providing subtitles and text that are easy for viewers to read.

[0032] The key point extraction unit can automatically detect important lines and scenes in a video and add subtitles and highlights to prevent viewers from missing them. For example, the generation AI collects viewer gaze tracking data in real time and highlights important points when the viewer's gaze shifts. The key point extraction unit can also highlight important points based on the viewer's emotional state. For example, using emotion estimation, it can use soft colors and fonts when the viewer is relaxed and calm colors and fonts when the viewer is tense. The key point extraction unit can also use viewer gaze tracking data to highlight important points when the viewer shifts their gaze. For example, it can change the color of important text the moment the viewer shifts their gaze, drawing the viewer's attention. This automatically detects important lines and scenes, preventing viewers from missing them and improving the viewing experience.

[0033] The color adjustment unit can use viewer gaze tracking data to adjust colors in real time according to the degree of gaze concentration. For example, the color adjustment unit collects viewer gaze tracking data in real time and emphasizes the color of the part where the gaze is focused. For example, if the gaze is focused on specific text, the color of the text is brightened. The color adjustment unit can also use viewer gaze tracking data to adjust colors in real time according to the degree of gaze concentration. For example, if the gaze is focused on specific text, the color of the text is brightened. The color adjustment unit can also automatically adjust colors to be optimal for different devices (smartphones, tablets, PCs, etc.). For example, the color adjustment unit detects the screen size and resolution of the device and optimizes colors accordingly. This makes it possible to provide visual comfort by adjusting colors in real time using viewer gaze tracking data.

[0034] The font adjustment unit can analyze a viewer's past viewing history and personalize the font that is optimal for each viewer. The font adjustment unit, for example, analyzes a viewer's past viewing history and identifies the font that the viewer prefers. For example, it extracts a font pattern that the viewer prefers from the viewing history and adjusts the font based on that. The font adjustment unit can also analyze a viewer's past viewing history and personalize the font that is optimal for each viewer. For example, it extracts a font pattern that the viewer prefers from the viewing history and adjusts the font based on that. The font adjustment unit can also adjust fonts specifically for visually impaired people, for example, to improve accessibility. For example, it can automatically select a high-contrast font for visually impaired people. In this way, visual comfort can be provided by personalizing fonts by analyzing a viewer's past viewing history.

[0035] The color adjustment unit can automatically adjust colors to suit different devices. For example, the color adjustment unit automatically adjusts colors to suit different devices (smartphones, tablets, PCs, etc.). For example, the generation AI detects the screen size and resolution of the device and optimizes colors accordingly. The color adjustment unit can also use, for example, viewer eye-tracking data to adjust colors in real time according to the level of gaze concentration. For example, if the gaze is focused on specific text, the color of that text can be brightened. The color adjustment unit can also automatically adjust colors to suit different devices (smartphones, tablets, PCs, etc.). For example, it can detect the screen size and resolution of the device and optimize colors accordingly. This allows for visual consistency to be maintained by automatically adjusting colors to suit different devices.

[0036] In addition to adjusting colors and fonts, generative AI also automates the layout and animation of design elements, achieving further cost reductions. In addition to adjusting colors and fonts, generative AI can, for example, automatically generate layouts. For example, it can automatically create visually balanced layouts, saving designers time and effort. Generative AI can also, for example, automatically generate animations. For example, it can automatically create visually appealing animations, saving designers time and effort. This allows generative AI to automate the layout and animation of design elements in addition to adjusting colors and fonts, achieving further cost reductions.

[0037] When optimizing a design, generative AI introduces algorithms that minimize energy consumption, thereby reducing operational costs. Generative AI, for example, optimizes a design using an energy-efficient algorithm. For example, by minimizing computing resources, energy consumption is reduced. Generative AI also introduces algorithms that minimize energy consumption, thereby reducing operational costs. For example, it measures power consumption and uses efficient algorithms. By introducing algorithms that minimize energy consumption, operational costs can be reduced.

[0038] Generative AI automates the design of advertising content, reducing advertising production costs. For example, generative AI automatically generates advertising content designs, reducing the effort required for advertising production. For example, it automatically creates designs for banner ads and video ads. Generative AI also automates the design of advertising content, reducing advertising production costs. For example, it shortens production time and optimizes resources. In this way, automating the design of advertising content can reduce advertising production costs.

[0039] Generative AI supports different media formats and reduces the production costs of all media content. Generative AI supports different media formats, such as video, images, and text, and automatically generates each content. For example, it automatically creates thumbnail images and text captions for videos. Generative AI also supports different media formats and reduces the production costs of all media content. For example, it shortens production time and optimizes resources. This makes it possible to support different media formats and reduce the production costs of all media content.

[0040] The generative AI learns the visual preferences of different cultural spheres and automatically selects colors and fonts appropriate to the culture. The generative AI, for example, learns the visual preferences of different cultural spheres and automatically selects colors appropriate to the culture. For example, red is used frequently in Asia, while blue is used frequently in Western countries. The generative AI also learns the visual preferences of different cultural spheres and automatically selects fonts appropriate to the culture. For example, Mincho font is used in Asia, while serif fonts are used in Western countries. In this way, visual consistency can be maintained by learning the visual preferences of different cultural spheres and automatically selecting colors and fonts appropriate to the culture.

[0041] Generative AI generates multilingual subtitles in real time, allowing viewers to understand immediately. Generative AI, for example, generates multilingual subtitles in real time, allowing viewers to understand immediately. For example, it automatically generates Japanese and French subtitles for an English video. Generative AI also generates multilingual subtitles in real time, allowing viewers to understand immediately. For example, it generates subtitles using translation technology. In this way, multilingual subtitles can be generated in real time, allowing viewers to understand immediately.

[0042] The generation AI analyzes trends in different regions and automatically generates the most suitable content for each region. For example, the generation AI analyzes trends in different regions in real time and automatically generates the most suitable content for each region. For example, it generates content based on themes that are popular in Asia. The generation AI also analyzes trends in different regions and automatically generates the most suitable content for each region. For example, it analyzes trends based on social media data. This improves the viewing experience by analyzing trends in different regions and automatically generating the most suitable content for each region.

[0043] The generation AI synthesizes voices in different languages, allowing viewers to enjoy content in their native language. The generation AI, for example, synthesizes voices in different languages, allowing viewers to enjoy content in their native language. For example, it automatically generates Japanese or French voices for an English video. The generation AI also synthesizes voices in different languages, allowing viewers to enjoy content in their native language. For example, it generates voices using text-to-speech synthesis technology. In this way, by synthesizing voices in different languages, viewers can enjoy content in their native language.

[0044] The generation AI uses viewer gaze tracking data to highlight important points when their gaze moves away. The generation AI, for example, collects viewer gaze tracking data in real time and highlights important points when their gaze moves away. For example, it may change the color of important text the moment their gaze moves away, thereby attracting the viewer's attention. The generation AI also uses viewer gaze tracking data to highlight important points when their gaze moves away. For example, it may change the color of important text the moment their gaze moves away, thereby attracting the viewer's attention. This improves the viewing experience by highlighting important points when the viewer moves away.

[0045] The generation AI can automatically adjust the optimal key point display for different devices. For example, the generation AI can automatically adjust the optimal key point display for different devices (smartphones, tablets, PCs, etc.). For example, it can detect the device's screen size and resolution and optimize the key point display accordingly. The generation AI can also use viewer eye-tracking data to adjust the key point display in real time according to the level of gaze concentration. For example, if the gaze is focused on specific text, the color of that text can be brightened. This makes it possible to maintain visual consistency by automatically adjusting the optimal key point display for different devices.

[0046] The generation AI can improve accessibility by displaying key points specialized for the visually impaired. The generation AI can improve accessibility by displaying key points specialized for the visually impaired, for example. For example, it can automatically select high-contrast colors and large fonts for the visually impaired. The generation AI can also improve accessibility by displaying key points specialized for the visually impaired, for example. For example, it can use a color scheme that makes it easy for viewers with color vision deficiency to distinguish colors. In this way, accessibility is improved by displaying key points specialized for the visually impaired.

[0047] The generation AI can optimize the display position and timing of subtitles in real time based on viewer gaze tracking data. For example, the generation AI collects viewer gaze tracking data in real time and displays subtitles in areas where the gaze is concentrated. For example, if the gaze is concentrated at the bottom of the screen, the subtitles will be displayed at the bottom. The generation AI also optimizes the display position and timing of subtitles in real time based on viewer gaze tracking data. For example, if the gaze is concentrated at the bottom of the screen, the subtitles will be displayed at the bottom. This improves the viewing experience by optimizing the display position and timing of subtitles in real time based on viewer gaze tracking data.

[0048] The generation AI can analyze the viewer's past viewing history and personalize the subtitle display to suit each individual viewer. For example, the generation AI can analyze the viewer's past viewing history and identify the viewer's preferred subtitle display position and timing. For example, it can extract the viewer's preferred subtitle display pattern from the viewing history and display subtitles based on that. The generation AI can also analyze the viewer's past viewing history and personalize the subtitle display to suit each individual viewer. For example, it can extract the viewer's preferred subtitle display pattern from the viewing history and display subtitles based on that. In this way, the viewing experience can be improved by personalizing the subtitle display by analyzing the viewer's past viewing history.

[0049] The generation AI can automatically adjust the subtitle display to suit different devices. For example, the generation AI can detect the device's screen size and resolution and optimize the subtitle display accordingly. The generation AI can also use viewer eye-tracking data to adjust the subtitle display in real time according to the viewer's gaze concentration. For example, if the gaze is focused on specific text, the color of that text will be brighter. This allows the subtitle display to be automatically adjusted to suit different devices, maintaining visual consistency.

[0050] Generative AI can improve accessibility by displaying subtitles specialized for the visually impaired. Generative AI can improve accessibility by displaying subtitles specialized for the visually impaired. For example, it can automatically select high-contrast colors and large fonts for the visually impaired. Generative AI can also improve accessibility by displaying subtitles specialized for the visually impaired. For example, it can use a color scheme that makes it easy for viewers with color blindness to distinguish colors. This improves accessibility by displaying subtitles specialized for the visually impaired.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The media content adjustment system may further include an audio adjustment unit. The audio adjustment unit, for example, automatically adjusts the tone and volume of audio based on the viewer's hearing characteristics. For example, audio adjustment specialized for the hearing impaired may be performed to improve audio clarity. The audio adjustment unit may also analyze the viewer's past viewing history and personalize optimal audio settings for each individual viewer. For example, the audio adjustment unit may extract the viewer's preferred audio tone and volume patterns from the viewing history and adjust the audio based on those patterns. This provides the viewer with an optimal audio environment, improving the viewing experience.

[0053] The media content adjustment system may further include a content recommendation unit. The content recommendation unit recommends optimal content to a viewer based on, for example, the viewer's past viewing history and preferences. For example, the content recommendation unit may extract the viewer's favorite genres and themes from the viewing history and recommend new content based on the extracted genres and themes. The content recommendation unit may also analyze the viewer's real-time viewing behavior and recommend related content while the viewer is viewing content. For example, the content recommendation unit may recommend documentaries or interviews related to the content currently being viewed. This improves the viewing experience by providing content that is interesting to the viewer.

[0054] The media content adjustment system may further include an interactive element adding unit. The interactive element adding unit may, for example, add interactive elements that allow viewers to react to the content in real time. For example, the interactive element adding unit may provide a function that allows viewers to post comments on specific scenes. The interactive element adding unit may also provide a function that allows viewers to select options that affect the progression of the content. For example, the interactive element adding unit may provide an interactive video in which the story development changes depending on the options selected by the viewer. This allows viewers to actively participate in the content, improving the viewing experience.

[0055] The media content adjustment system can further include an audio guide unit specialized for visually impaired people. The audio guide unit, for example, provides audio guides specialized for visually impaired people to help them understand the content. For example, it provides audio descriptions of scenes and explanations of important points. The audio guide unit can also provide audio guides specialized for visually impaired people to improve accessibility. For example, it can automatically select high-contrast colors and large fonts for visually impaired people. This improves accessibility by providing audio guides specialized for visually impaired people.

[0056] The media content adjustment system can also learn the visual preferences of different cultures and automatically select colors and fonts appropriate to the culture. For example, Asian countries use more red, while Western countries use more blue. It can also learn the visual preferences of different cultures and automatically select fonts appropriate to the culture. For example, Asian countries use Mincho fonts, while Western countries use serif fonts. In this way, visual consistency can be maintained by learning the visual preferences of different cultures and automatically selecting colors and fonts appropriate to the culture.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The color adjustment unit automatically adjusts the color of media content. For example, it selects the optimal color for each video scene to provide visually appealing content. It can also use viewer eye tracking data to adjust the color in real time according to the viewer's gaze concentration. It can also automatically adjust the color to suit different devices (smartphones, tablets, PCs, etc.). Step 2: The font adjustment unit automatically adjusts the font based on the color adjusted by the color adjustment unit. For example, it can provide subtitles and text that are easy for viewers to read. It can also analyze viewers' past viewing history and personalize the optimal font for each individual viewer. It can also adjust fonts specifically for the visually impaired to improve accessibility. Step 3: The key point extraction unit extracts important points based on the font adjusted by the font adjustment unit. For example, it can automatically detect important lines and scenes in the video and add subtitles or highlights to ensure viewers don't miss them. It can also highlight important points based on the viewer's emotional state. It can also use viewer gaze tracking data to highlight important points when the viewer's gaze shifts.

[0059] (Example 2) The media content adjustment system according to an embodiment of the present invention uses generative AI to automatically adjust fine details such as color and font of media content, thereby accelerating cost reduction and media globalization, and enabling viewers to enjoy the content without missing important points regardless of language.

[0060] A media content adjustment system according to an embodiment includes a generation AI, a color adjustment unit, a font adjustment unit, and a key point extraction unit. The generation AI automatically adjusts the color and font of media content. The color adjustment unit, for example, selects optimal colors according to video scenes to provide visually appealing content. The color adjustment unit can also use, for example, viewer gaze tracking data to adjust colors in real time according to the level of gaze concentration. The color adjustment unit can also automatically adjust colors to suit different devices (smartphones, tablets, PCs, etc.). The font adjustment unit, for example, provides subtitles and text that are easy for viewers to read. The font adjustment unit can also analyze, for example, viewers' past viewing history to personalize fonts that are optimal for each individual viewer. The font adjustment unit can also improve accessibility by adjusting fonts specifically for visually impaired people. The key point extraction unit, for example, automatically detects important lines and scenes in a video and adds subtitles and highlights to prevent viewers from missing them. The key point extraction unit can also highlight important points according to the viewer's emotional state, for example. The key point extraction unit can also highlight important points when the viewer's gaze shifts, for example, by using viewer gaze tracking data. As a result, the media content adjustment system according to the embodiment automatically adjusts the color and font of media content and extracts important points, allowing viewers to grasp important information regardless of language.

[0061] The color adjustment unit can select the optimal color according to the scene in the video and provide visually appealing content. For example, the color adjustment unit can select the optimal color according to the scene in the video and provide visually appealing content. For example, the generation AI can collect viewer gaze tracking data in real time and emphasize the color of the part where the gaze is focused. The color adjustment unit can also use the viewer gaze tracking data to adjust the color in real time according to the degree of gaze concentration. For example, if the gaze is focused on specific text, the color of that text can be brightened. The color adjustment unit can also automatically adjust the optimal color for different devices (smartphones, tablets, PCs, etc.). For example, it can detect the screen size and resolution of the device and optimize the color accordingly. This allows the optimal color to be selected according to the scene in the video, providing visually appealing content.

[0062] The font adjustment unit can provide subtitles and text that are easy for viewers to read. For example, the generation AI can analyze a viewer's past viewing history to identify the font that the viewer prefers. The font adjustment unit can also analyze a viewer's past viewing history to personalize the optimal font for each viewer. For example, it can extract font patterns that the viewer prefers from the viewing history and adjust the font based on that. The font adjustment unit can also improve accessibility by adjusting fonts specifically for the visually impaired. For example, it can automatically select a high-contrast font for the visually impaired. This improves the viewing experience by providing subtitles and text that are easy for viewers to read.

[0063] The key point extraction unit can automatically detect important lines and scenes in a video and add subtitles and highlights to prevent viewers from missing them. For example, the generation AI collects viewer gaze tracking data in real time and highlights important points when the viewer's gaze shifts. The key point extraction unit can also highlight important points based on the viewer's emotional state. For example, using emotion estimation, it can use soft colors and fonts when the viewer is relaxed and calm colors and fonts when the viewer is tense. The key point extraction unit can also use viewer gaze tracking data to highlight important points when the viewer shifts their gaze. For example, it can change the color of important text the moment the viewer shifts their gaze, drawing the viewer's attention. This automatically detects important lines and scenes, preventing viewers from missing them and improving the viewing experience.

[0064] The color adjustment unit can use viewer gaze tracking data to adjust colors in real time according to the degree of gaze concentration. For example, the color adjustment unit collects viewer gaze tracking data in real time and emphasizes the color of the part where the gaze is focused. For example, if the gaze is focused on specific text, the color of the text is brightened. The color adjustment unit can also use viewer gaze tracking data to adjust colors in real time according to the degree of gaze concentration. For example, if the gaze is focused on specific text, the color of the text is brightened. The color adjustment unit can also automatically adjust colors to be optimal for different devices (smartphones, tablets, PCs, etc.). For example, the color adjustment unit detects the screen size and resolution of the device and optimizes colors accordingly. This makes it possible to provide visual comfort by adjusting colors in real time using viewer gaze tracking data.

[0065] The font adjustment unit can analyze a viewer's past viewing history and personalize the font that is optimal for each viewer. The font adjustment unit, for example, analyzes a viewer's past viewing history and identifies the font that the viewer prefers. For example, it extracts a font pattern that the viewer prefers from the viewing history and adjusts the font based on that. The font adjustment unit can also analyze a viewer's past viewing history and personalize the font that is optimal for each viewer. For example, it extracts a font pattern that the viewer prefers from the viewing history and adjusts the font based on that. The font adjustment unit can also adjust fonts specifically for visually impaired people, for example, to improve accessibility. For example, it can automatically select a high-contrast font for visually impaired people. In this way, visual comfort can be provided by personalizing fonts by analyzing a viewer's past viewing history.

[0066] The key point extraction unit can highlight important points according to the viewer's emotional state. The key point extraction unit, for example, highlights important points according to the viewer's emotional state. For example, by using an emotion estimation function, soft colors and fonts are used when the viewer is relaxed, and subdued colors and fonts are used when the viewer is tense. The key point extraction unit can also highlight important points when the viewer's gaze averts, for example, by using viewer gaze tracking data. For example, the color of important text can be changed the moment the viewer averts their gaze to attract the viewer's attention. The key point extraction unit can also highlight important points according to the viewer's emotional state, for example. For example, by using an emotion estimation function, soft colors and fonts are used when the viewer is relaxed, and subdued colors and fonts are used when the viewer is tense. This improves the viewing experience by highlighting important points according to the viewer's emotional state.

[0067] The color adjustment unit can automatically adjust colors to suit different devices. For example, the color adjustment unit automatically adjusts colors to suit different devices (smartphones, tablets, PCs, etc.). For example, the generation AI detects the screen size and resolution of the device and optimizes colors accordingly. The color adjustment unit can also use, for example, viewer eye-tracking data to adjust colors in real time according to the level of gaze concentration. For example, if the gaze is focused on specific text, the color of that text can be brightened. The color adjustment unit can also automatically adjust colors to suit different devices (smartphones, tablets, PCs, etc.). For example, it can detect the screen size and resolution of the device and optimize colors accordingly. This allows for visual consistency to be maintained by automatically adjusting colors to suit different devices.

[0068] The key point extraction unit can use the emotion estimation function to dynamically change key points based on the emotion felt by the viewer in a particular scene. The key point extraction unit, for example, uses the emotion estimation function to dynamically change key points based on the emotion felt by the viewer in a particular scene. For example, important points can be highlighted in a scene in which the viewer is moved, and a calm display in a scene in which the viewer is tense. For example, important text can be highlighted in red in an emotional scene. The key point extraction unit can also highlight important points according to the viewer's emotional state, for example. For example, using the emotion estimation function, soft colors and fonts can be used when the viewer is relaxed, and calm colors and fonts can be used when the viewer is tense. In this way, the viewing experience can be improved by dynamically changing key points based on the viewer's emotion.

[0069] In addition to adjusting colors and fonts, generative AI also automates the layout and animation of design elements, achieving further cost reductions. In addition to adjusting colors and fonts, generative AI can, for example, automatically generate layouts. For example, it can automatically create visually balanced layouts, saving designers time and effort. Generative AI can also, for example, automatically generate animations. For example, it can automatically create visually appealing animations, saving designers time and effort. This allows generative AI to automate the layout and animation of design elements in addition to adjusting colors and fonts, achieving further cost reductions.

[0070] When optimizing a design, generative AI introduces algorithms that minimize energy consumption, thereby reducing operational costs. Generative AI, for example, optimizes a design using an energy-efficient algorithm. For example, by minimizing computing resources, energy consumption is reduced. Generative AI also introduces algorithms that minimize energy consumption, thereby reducing operational costs. For example, it measures power consumption and uses efficient algorithms. By introducing algorithms that minimize energy consumption, operational costs can be reduced.

[0071] The emotion estimation function analyzes the emotional responses of viewers and selects the most cost-effective design. For example, the emotion estimation function identifies designs that viewers have positive emotions about and prioritizes the use of those designs. For example, it selects designs based on the colors and fonts that viewers prefer. The emotion estimation function also analyzes the emotional responses of viewers and selects the most cost-effective design. For example, it analyzes cost performance and optimizes resources. This makes it possible to reduce costs by analyzing the emotional responses of viewers and selecting the most cost-effective design.

[0072] Generative AI automates the design of advertising content, reducing advertising production costs. For example, generative AI automatically generates advertising content designs, reducing the effort required for advertising production. For example, it automatically creates designs for banner ads and video ads. Generative AI also automates the design of advertising content, reducing advertising production costs. For example, it shortens production time and optimizes resources. In this way, automating the design of advertising content can reduce advertising production costs.

[0073] Generative AI supports different media formats and reduces the production costs of all media content. Generative AI supports different media formats, such as video, images, and text, and automatically generates each content. For example, it automatically creates thumbnail images and text captions for videos. Generative AI also supports different media formats and reduces the production costs of all media content. For example, it shortens production time and optimizes resources. This makes it possible to support different media formats and reduce the production costs of all media content.

[0074] The emotion estimation function optimizes ad design based on viewer emotions to maximize advertising effectiveness. For example, the emotion estimation function identifies ad designs that evoke positive emotions in viewers and prioritizes the use of those designs. For example, it designs ads based on the viewer's preferred colors and fonts. The emotion estimation function also optimizes ad design based on viewer emotions to maximize advertising effectiveness. For example, it aims to improve click rates and conversion rates. In this way, advertising effectiveness can be maximized by optimizing ad design based on viewer emotions.

[0075] The generative AI learns the visual preferences of different cultural spheres and automatically selects colors and fonts appropriate to the culture. The generative AI, for example, learns the visual preferences of different cultural spheres and automatically selects colors appropriate to the culture. For example, red is used frequently in Asia, while blue is used frequently in Western countries. The generative AI also learns the visual preferences of different cultural spheres and automatically selects fonts appropriate to the culture. For example, Mincho font is used in Asia, while serif fonts are used in Western countries. In this way, visual consistency can be maintained by learning the visual preferences of different cultural spheres and automatically selecting colors and fonts appropriate to the culture.

[0076] Generative AI generates multilingual subtitles in real time, allowing viewers to understand immediately. Generative AI, for example, generates multilingual subtitles in real time, allowing viewers to understand immediately. For example, it automatically generates Japanese and French subtitles for an English video. Generative AI also generates multilingual subtitles in real time, allowing viewers to understand immediately. For example, it generates subtitles using translation technology. In this way, multilingual subtitles can be generated in real time, allowing viewers to understand immediately.

[0077] The emotion estimation function analyzes the emotional responses of viewers from different cultural spheres and provides optimal content. The emotion estimation function, for example, analyzes the emotional responses of viewers from different cultural spheres in real time and provides optimal content. For example, it highlights scenes that move viewers. The emotion estimation function also analyzes the emotional responses of viewers from different cultural spheres and provides optimal content. For example, it selects content based on the viewer's emotional response. In this way, the viewing experience is improved by analyzing the emotional responses of viewers from different cultural spheres and providing optimal content.

[0078] The generation AI analyzes trends in different regions and automatically generates the most suitable content for each region. For example, the generation AI analyzes trends in different regions in real time and automatically generates the most suitable content for each region. For example, it generates content based on themes that are popular in Asia. The generation AI also analyzes trends in different regions and automatically generates the most suitable content for each region. For example, it analyzes trends based on social media data. This improves the viewing experience by analyzing trends in different regions and automatically generating the most suitable content for each region.

[0079] The generation AI synthesizes voices in different languages, allowing viewers to enjoy content in their native language. The generation AI, for example, synthesizes voices in different languages, allowing viewers to enjoy content in their native language. For example, it automatically generates Japanese or French voices for an English video. The generation AI also synthesizes voices in different languages, allowing viewers to enjoy content in their native language. For example, it generates voices using text-to-speech synthesis technology. In this way, by synthesizing voices in different languages, viewers can enjoy content in their native language.

[0080] The emotion estimation function automatically generates content that viewers from different cultural backgrounds can easily relate to. The emotion estimation function, for example, automatically generates content that viewers from different cultural backgrounds can easily relate to. For example, it highlights scenes that move viewers. The emotion estimation function also automatically generates content that viewers from different cultural backgrounds can easily relate to. For example, it selects content based on the viewer's emotional reactions. This automatically generates content that viewers from different cultural backgrounds can easily relate to, improving the viewing experience.

[0081] The generation AI uses viewer gaze tracking data to highlight important points when their gaze moves away. The generation AI, for example, collects viewer gaze tracking data in real time and highlights important points when their gaze moves away. For example, it may change the color of important text the moment their gaze moves away, thereby attracting the viewer's attention. The generation AI also uses viewer gaze tracking data to highlight important points when their gaze moves away. For example, it may change the color of important text the moment their gaze moves away, thereby attracting the viewer's attention. This improves the viewing experience by highlighting important points when the viewer moves away.

[0082] The emotion estimation function can highlight important points according to the viewer's emotional state. The emotion estimation function can, for example, highlight important points according to the viewer's emotional state. For example, soft colors and fonts are used when the viewer is relaxed, and calm colors and fonts are used when the viewer is tense. The emotion estimation function can also highlight important points according to the viewer's emotional state. For example, soft colors and fonts are used when the viewer is relaxed, and calm colors and fonts are used when the viewer is tense. In this way, the viewing experience is improved by highlighting important points according to the viewer's emotional state.

[0083] The generation AI can automatically adjust the optimal key point display for different devices. For example, the generation AI can automatically adjust the optimal key point display for different devices (smartphones, tablets, PCs, etc.). For example, it can detect the device's screen size and resolution and optimize the key point display accordingly. The generation AI can also use viewer eye-tracking data to adjust the key point display in real time according to the level of gaze concentration. For example, if the gaze is focused on specific text, the color of that text can be brightened. This makes it possible to maintain visual consistency by automatically adjusting the optimal key point display for different devices.

[0084] The generation AI can improve accessibility by displaying key points specialized for the visually impaired. The generation AI can improve accessibility by displaying key points specialized for the visually impaired, for example. For example, it can automatically select high-contrast colors and large fonts for the visually impaired. The generation AI can also improve accessibility by displaying key points specialized for the visually impaired, for example. For example, it can use a color scheme that makes it easy for viewers with color vision deficiency to distinguish colors. In this way, accessibility is improved by displaying key points specialized for the visually impaired.

[0085] The emotion estimation function can dynamically change key points based on the emotions felt by the viewer in a particular scene. For example, the emotion estimation function can highlight important points in a scene in which the viewer is moved, and display them in a calmer tone in a scene in which the viewer is tense. For example, important text can be highlighted in red in an emotional scene. The emotion estimation function can also highlight important points according to the viewer's emotional state, for example. For example, soft colors and fonts are used when the viewer is relaxed, and calm colors and fonts are used when the viewer is tense. This improves the viewing experience by dynamically changing key points based on the viewer's emotions.

[0086] The generation AI can optimize the display position and timing of subtitles in real time based on viewer gaze tracking data. For example, the generation AI collects viewer gaze tracking data in real time and displays subtitles in areas where the gaze is concentrated. For example, if the gaze is concentrated at the bottom of the screen, the subtitles will be displayed at the bottom. The generation AI also optimizes the display position and timing of subtitles in real time based on viewer gaze tracking data. For example, if the gaze is concentrated at the bottom of the screen, the subtitles will be displayed at the bottom. This improves the viewing experience by optimizing the display position and timing of subtitles in real time based on viewer gaze tracking data.

[0087] The generation AI can analyze the viewer's past viewing history and personalize the subtitle display to suit each individual viewer. For example, the generation AI can analyze the viewer's past viewing history and identify the viewer's preferred subtitle display position and timing. For example, it can extract the viewer's preferred subtitle display pattern from the viewing history and display subtitles based on that. The generation AI can also analyze the viewer's past viewing history and personalize the subtitle display to suit each individual viewer. For example, it can extract the viewer's preferred subtitle display pattern from the viewing history and display subtitles based on that. In this way, the viewing experience can be improved by personalizing the subtitle display by analyzing the viewer's past viewing history.

[0088] The emotion estimation function can adjust the content and display method of subtitles according to the emotional state of the viewer. The emotion estimation function can adjust the content and display method of subtitles according to the emotional state of the viewer, for example. For example, soft colors and fonts are used when the viewer is relaxed, and calm colors and fonts are used when the viewer is tense. The emotion estimation function can also adjust the content and display method of subtitles according to the emotional state of the viewer, for example. For example, soft colors and fonts are used when the viewer is relaxed, and calm colors and fonts are used when the viewer is tense. In this way, the viewing experience is improved by adjusting the content and display method of subtitles according to the emotional state of the viewer.

[0089] The generation AI can automatically adjust the subtitle display to suit different devices. For example, the generation AI can detect the device's screen size and resolution and optimize the subtitle display accordingly. The generation AI can also use viewer eye-tracking data to adjust the subtitle display in real time according to the viewer's gaze concentration. For example, if the gaze is focused on specific text, the color of that text will be brighter. This allows the subtitle display to be automatically adjusted to suit different devices, maintaining visual consistency.

[0090] Generative AI can improve accessibility by displaying subtitles specialized for the visually impaired. Generative AI can improve accessibility by displaying subtitles specialized for the visually impaired. For example, it can automatically select high-contrast colors and large fonts for the visually impaired. Generative AI can also improve accessibility by displaying subtitles specialized for the visually impaired. For example, it can use a color scheme that makes it easy for viewers with color blindness to distinguish colors. This improves accessibility by displaying subtitles specialized for the visually impaired.

[0091] The emotion estimation function can dynamically change the content and display method of subtitles based on the emotions felt by the viewer in a particular scene. For example, the emotion estimation function dynamically changes the content and display method of subtitles based on the emotions felt by the viewer in a particular scene. For example, the content of the subtitles may be highlighted in a scene in which the viewer is moved, and displayed in a calm manner in a scene in which the viewer is tense. For example, the subtitles may be highlighted in red in an emotional scene. The emotion estimation function can also adjust the content and display method of subtitles according to the viewer's emotional state, for example. For example, soft colors and fonts may be used when the viewer is relaxed, and calm colors and fonts may be used when the viewer is tense. This improves the viewing experience by dynamically changing the content and display method of subtitles based on the viewer's emotions.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The media content adjustment system may further include an audio adjustment unit. The audio adjustment unit, for example, automatically adjusts the tone and volume of audio based on the viewer's hearing characteristics. For example, audio adjustment specialized for the hearing impaired may be performed to improve audio clarity. The audio adjustment unit may also analyze the viewer's past viewing history and personalize optimal audio settings for each individual viewer. For example, the audio adjustment unit may extract the viewer's preferred audio tone and volume patterns from the viewing history and adjust the audio based on those patterns. This provides the viewer with an optimal audio environment, improving the viewing experience.

[0094] The media content adjustment system may further include a content recommendation unit. The content recommendation unit recommends optimal content to a viewer based on, for example, the viewer's past viewing history and preferences. For example, the content recommendation unit may extract the viewer's favorite genres and themes from the viewing history and recommend new content based on the extracted genres and themes. The content recommendation unit may also analyze the viewer's real-time viewing behavior and recommend related content while the viewer is viewing content. For example, the content recommendation unit may recommend documentaries or interviews related to the content currently being viewed. This improves the viewing experience by providing content that is interesting to the viewer.

[0095] The media content adjustment system may further include an interactive element adding unit. The interactive element adding unit may, for example, add interactive elements that allow viewers to react to the content in real time. For example, the interactive element adding unit may provide a function that allows viewers to post comments on specific scenes. The interactive element adding unit may also provide a function that allows viewers to select options that affect the progression of the content. For example, the interactive element adding unit may provide an interactive video in which the story development changes depending on the options selected by the viewer. This allows viewers to actively participate in the content, improving the viewing experience.

[0096] The media content adjustment system can further use an emotion estimation function to optimize advertisements based on the viewer's emotions. For example, when a viewer is relaxed, an advertisement with a relaxing effect is displayed, and when a viewer is tense, an advertisement that promotes relaxation is displayed. The emotion estimation function can also be used to identify advertisement designs that evoke positive emotions in the viewer and prioritize their use. For example, advertisements can be designed based on the viewer's preferred colors and fonts. This allows the effectiveness of advertisements to be maximized by optimizing them based on the viewer's emotions.

[0097] The media content adjustment system can further use an emotion estimation function to adjust the playback speed of content based on the viewer's emotions. For example, the playback speed can be slowed down when the viewer is relaxed and increased when the viewer is nervous. The emotion estimation function can also be used to slow down the playback speed of scenes that move the viewer and highlight important points. This improves the viewing experience by adjusting the playback speed of content based on the viewer's emotions.

[0098] The media content adjustment system can further use an emotion estimation function to adjust the tone and volume of the audio based on the viewer's emotions. For example, a soft tone and low volume can be used when the viewer is relaxed, and a calm tone and appropriate volume can be used when the viewer is nervous. The emotion estimation function can also be used to raise the tone of the audio in scenes that move the viewer, emphasizing important points. This improves the viewing experience by adjusting the tone and volume of the audio based on the viewer's emotions.

[0099] The media content adjustment system can also use emotion estimation to adjust the content and display of subtitles based on the viewer's emotions. For example, soft colors and fonts can be used when the viewer is relaxed, and subdued colors and fonts when the viewer is tense. The emotion estimation function can also be used to highlight subtitle content in scenes that move the viewer, emphasizing important points. This improves the viewing experience by adjusting the content and display of subtitles based on the viewer's emotions.

[0100] The media content adjustment system can further use an emotion estimation function to adjust the content layout based on the viewer's emotions. For example, a soft layout can be used when the viewer is relaxed, and a calm layout when the viewer is nervous. The emotion estimation function can also be used to change the layout in scenes that move the viewer, emphasizing important points. This improves the viewing experience by adjusting the content layout based on the viewer's emotions.

[0101] The media content adjustment system can further include an audio guide unit specialized for visually impaired people. The audio guide unit, for example, provides audio guides specialized for visually impaired people to help them understand the content. For example, it provides audio descriptions of scenes and explanations of important points. The audio guide unit can also provide audio guides specialized for visually impaired people to improve accessibility. For example, it can automatically select high-contrast colors and large fonts for visually impaired people. This improves accessibility by providing audio guides specialized for visually impaired people.

[0102] The media content adjustment system can also learn the visual preferences of different cultures and automatically select colors and fonts appropriate to the culture. For example, Asian countries use more red, while Western countries use more blue. It can also learn the visual preferences of different cultures and automatically select fonts appropriate to the culture. For example, Asian countries use Mincho fonts, while Western countries use serif fonts. In this way, visual consistency can be maintained by learning the visual preferences of different cultures and automatically selecting colors and fonts appropriate to the culture.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The color adjustment unit automatically adjusts the color of media content. For example, it selects the optimal color for each video scene to provide visually appealing content. It can also use viewer eye tracking data to adjust the color in real time according to the viewer's gaze concentration. It can also automatically adjust the color to suit different devices (smartphones, tablets, PCs, etc.). Step 2: The font adjustment unit automatically adjusts the font based on the color adjusted by the color adjustment unit. For example, it can provide subtitles and text that are easy for viewers to read. It can also analyze viewers' past viewing history and personalize the optimal font for each individual viewer. It can also adjust fonts specifically for the visually impaired to improve accessibility. Step 3: The key point extraction unit extracts important points based on the font adjusted by the font adjustment unit. For example, it can automatically detect important lines and scenes in the video and add subtitles or highlights to ensure viewers don't miss them. It can also highlight important points based on the viewer's emotional state. It can also use viewer gaze tracking data to highlight important points when the viewer's gaze shifts.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 AI 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

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

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] 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.

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

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0137] The data processing system 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 AI 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] 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]

[0172] 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. Equipped with generative AI, The generated AI is A color adjustment unit that automatically adjusts the color of media content; a font adjustment unit that automatically adjusts a font based on the color adjusted by the color adjustment unit; a key point extraction unit that extracts important points based on the font adjusted by the font adjustment unit. A system characterized by:

2. The color adjustment unit Select the optimal color for each video scene to provide visually appealing content 2. The system of claim 1.

3. The font adjustment unit Analyzing the viewing history of each viewer and personalizing the font to suit each viewer.

2. The system of claim 1.

4. The keypoint extraction unit Automatically detects important lines and scenes in your videos and adds subtitles and highlights to ensure viewers don't miss anything 2. The system of claim 1.

5. The color adjustment unit Automatically adjust the color to suit different devices 2. The system of claim 1.

6. The keypoint extraction unit Dynamically change key points based on the emotion the viewer feels in a particular scene 2. The system of claim 1.

7. The generated AI is In addition to adjusting the colors and fonts, the layout and animation of design elements can also be automated, resulting in further cost savings.

2. The system of claim 1.

Images