System

Abstract

An object of the system according to the embodiment is to extract a specific scene from video content and generate a digest moving image.SOLUTION: A system according to an embodiment includes a video analysis unit, a scene extraction unit, and a digest generation unit. The video analysis unit analyzes video content. The scene extraction unit extracts a specific scene from the video content analyzed by the video analysis unit. The digest generation unit generates a digest moving image based on the scene extracted by the scene extraction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to efficiently extract specific scenes from video content and generate digest videos.

[0005] The system according to the embodiment aims to extract specific scenes from video content and generate a digest movie. [Means for solving the problem]

[0006] The system according to the embodiment includes a video analysis unit, a scene extraction unit, and a digest generation unit. The video analysis unit analyzes video content. The scene extraction unit extracts specific scenes from the video content analyzed by the video analysis unit. The digest generation unit generates a digest video based on the scenes extracted by the scene extraction unit. [Effects of the Invention]

[0007] The system according to the embodiment can extract specific scenes from video content and generate a digest movie. [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 digest video generation AI system according to an embodiment of the present invention is a system that can be used when a viewer wants to watch a specific scene or cannot watch the entire video due to time constraints. This system is realized by combining generative AI with conventional AI. As a result, the digest video generation AI system can generate digest videos that meet the viewer's needs and personalize the viewing experience.

[0029] The digest video generation AI system according to the embodiment includes a video analysis unit, a scene extraction unit, and a digest generation unit. The video analysis unit analyzes video content. For example, the video analysis unit performs frame analysis to detect objects within the video. The video analysis unit can also perform motion analysis to analyze movement within the video. The video analysis unit can also analyze audio data to detect specific keywords or phrases. For example, the video analysis unit uses voice recognition technology to convert audio data within the video into text data and extract specific keywords or phrases. The scene extraction unit extracts specific scenes from the video content analyzed by the video analysis unit. For example, the scene extraction unit extracts scenes based on important events or emotional moments. The scene extraction unit can also identify scenes with visual impact. The scene extraction unit can also simultaneously analyze video from different camera angles or viewpoints to extract the most appealing scenes. For example, the scene extraction unit compares video from multiple camera angles and extracts the most visually impactful scenes. The digest generation unit generates a digest video based on the scenes extracted by the scene extraction unit. For example, the digest generation unit arranges the extracted scenes in order and edits them so that the flow of the story can be understood. The digest generation unit can also automatically add music and sound effects to improve the viewing experience. The digest generation unit can also add effects such as adjusting the speed of the video and playing important scenes in slow motion. For example, the digest generation unit can play important scenes in slow motion to emphasize their visual impact. In this way, the digest video generation AI system according to the embodiment can generate digest videos that meet the viewer's needs and personalize the viewing experience. For example, if a user wants to watch only the climax scene of a movie or only the highlight scenes of a sports program, the system can be used to watch important scenes in a short amount of time. The viewing experience can also be improved by providing digest videos personalized to the user's preferences.

[0030] The video analysis unit can analyze audio data and extract scenes based on specific keywords or phrases. The video analysis unit, for example, converts audio data into text data using speech recognition technology. For example, speech recognition software automatically analyzes audio and saves it as text. The video analysis unit can also use natural language processing technology to extract specific keywords or phrases. For example, it can identify scenes based on frequently occurring words or important words. The video analysis unit can also perform acoustic feature analysis to detect specific phrases. For example, it can analyze the pitch and rhythm of audio to extract specific phrases. This makes it possible to extract scenes based on audio data.

[0031] The video analysis unit can analyze visual features and identify scenes that have a visual impact. The video analysis unit can, for example, analyze changes in color and identify scenes that have a visual impact. For example, it can extract scenes in which colors change suddenly. The video analysis unit can also analyze changes in shape and identify scenes that have a visual impact. For example, it can extract scenes in which shapes change suddenly. The video analysis unit can also analyze changes in movement and identify scenes that have a visual impact. For example, it can extract scenes in which movements change suddenly. In this way, it is possible to identify scenes that have a visual impact.

[0032] The scene extraction unit can simultaneously analyze video from different camera angles or viewpoints and extract the most attractive scene. For example, the scene extraction unit can simultaneously analyze video from multiple camera angles and identify the most attractive scene. For example, the scene extraction unit can compare video from multiple camera angles and extract the scene with the most visual impact. The scene extraction unit can also simultaneously analyze video from different viewpoints and identify the most attractive scene. For example, the scene extraction unit can compare video from a subjective viewpoint and an objective viewpoint and extract the most attractive scene. The scene extraction unit can also integrate video from different camera angles or viewpoints and identify the most attractive scene. For example, the scene extraction unit can integrate video from multiple camera angles and extract the scene with the most visual impact. This makes it possible to analyze video from different camera angles or viewpoints and extract the most attractive scene.

[0033] The scene extraction unit can analyze the facial expressions and movements of the characters and extract scenes that focus on a specific character. The scene extraction unit, for example, analyzes the facial expressions of the characters and extracts scenes that focus on a specific character. For example, scenes are identified based on facial expressions such as smiling or crying. The scene extraction unit can also analyze the movements of the characters and extract scenes that focus on a specific character. For example, scenes are identified based on movements such as walking or jumping. The scene extraction unit can also combine the facial expressions and movements of the characters to extract scenes that focus on a specific character. For example, a scene is identified based on a combination of smiling and walking. This makes it possible to extract scenes that focus on a specific character.

[0034] The digest generation unit can automatically add music and sound effects when generating a digest video to improve the viewing experience. The digest generation unit, for example, uses a generation AI to automatically add music and sound effects suitable for the digest video. For example, music that matches the atmosphere of a scene is selected and inserted into the video. The digest generation unit can also improve the viewing experience by adding sound effects. For example, sound effects can be added to action scenes to emphasize visual impact. The digest generation unit can also build a system that automatically adds music and sound effects. For example, the generation AI analyzes the atmosphere of a scene and selects appropriate music and sound effects. This makes it possible to improve the viewing experience by adding music and sound effects.

[0035] The digest generation unit can adjust the speed of the video when generating the digest video and add effects such as playing important scenes in slow motion. The digest generation unit can, for example, use generation AI to adjust the speed of the video when generating the digest video. For example, important scenes can be played in slow motion to emphasize their visual impact. The digest generation unit can also improve the viewing experience by adjusting the speed of the video. For example, action scenes can be played in slow motion to emphasize their visual impact. The digest generation unit can also build a system that adjusts the speed of the video. For example, the generation AI can analyze the importance of a scene and play it at an appropriate speed. This allows the speed of the video to be adjusted and important scenes to be emphasized.

[0036] The digest generation unit can generate cross-genre digest videos that combine video content of different genres. The digest generation unit generates, for example, cross-genre digest videos that combine video content of different genres. For example, it creates a video that combines highlights of a movie and a sports program. The digest generation unit can also improve the viewing experience by combining video content of different genres. For example, it creates a video that combines highlights of news and documentaries. The digest generation unit can also build a system that combines video content of different genres. For example, a generation AI analyzes videos of different genres and selects and combines appropriate scenes. This makes it possible to generate digest videos that combine video content of different genres.

[0037] The digest generation unit can add interactive elements to the digest video, allowing the user to select and view scenes. The digest generation unit can, for example, add interactive elements to the digest video, allowing the user to select and view scenes. For example, a selection button can be provided for each scene, allowing the user to select scenes of interest. The digest generation unit can also add an interactive menu, allowing the user to select and view scenes. For example, thumbnails of scenes can be displayed, allowing the user to click to view them. The digest generation unit can also build a system that adds interactive elements. For example, a generation AI can analyze the importance of scenes and add appropriate interactive elements. This makes it possible to provide an interactive digest video that allows the user to select and view scenes.

[0038] The system can analyze a user's viewing history and generate a personalized digest video based on their viewing habits. For example, the system can analyze a user's viewing history and generate a personalized digest video based on their viewing habits. For example, a user who watches a lot of action movies can be provided with videos containing many action scenes. The system can also analyze a user's preferences based on the viewing history and provide a personalized digest video. For example, the system can identify a user's favorite genre from the viewing history and provide videos containing many scenes of that genre. The system can also build a system that analyzes viewing history. For example, a generation AI can analyze the viewing history and select appropriate scenes for personalization. This makes it possible to generate a personalized digest video based on the user's viewing history.

[0039] The system can provide digest videos with optimal image quality and playback speed depending on the user's viewing environment. For example, the system can analyze the user's device and network conditions and provide digest videos with optimal image quality. For example, in a slow network environment, it can provide videos with lower image quality. The system can also adjust the playback speed depending on the user's viewing environment. For example, it can change the playback speed depending on the viewing environment to provide an optimal viewing experience. The system can also build a system that analyzes the viewing environment. For example, a generation AI can analyze the device and network conditions and select appropriate image quality and playback speed. This makes it possible to provide digest videos with optimal image quality and playback speed depending on the user's viewing environment.

[0040] The system can analyze a user's social media activity and generate a personalized digest video based on their interests. For example, the system can analyze a user's social media activity, such as posts and likes, and generate a personalized digest video based on their interests. For example, the system can provide videos that include many scenes related to a specific topic. The system can also analyze a user's interests based on their social media activity and provide a personalized digest video. For example, the system can select scenes based on the accounts the user follows or the groups the user joins. The system can also build a system that analyzes social media activity. For example, a generation AI can analyze social media data and select appropriate scenes for personalization. This makes it possible to generate a personalized digest video based on the user's social media activity.

[0041] The system can provide digest videos containing region-specific content based on the user's geographical location information. For example, the system can analyze the user's geographical location information and provide digest videos containing region-specific content. For example, videos containing scenes related to local news and events can be provided. The system can also analyze the user's interests based on the geographical location information and provide region-specific content. For example, videos containing scenes related to local culture and tourist spots can be provided. The system can also be constructed to analyze geographical location information. For example, a generation AI can analyze GPS data and IP addresses and select and provide appropriate scenes. This makes it possible to provide digest videos containing region-specific content based on the user's geographical location information.

[0042] The system can analyze the metadata of a VOD service and extract scenes based on detailed content information. For example, the system analyzes the metadata of a VOD service and extracts scenes based on detailed content information. For example, important scenes can be identified based on cast and staff information. The system can also analyze user preferences based on the metadata and extract appropriate scenes. For example, scenes can be selected based on viewing history and rating data. The system can also be built to analyze metadata. For example, a generation AI analyzes tag information and attribute data to select and extract appropriate scenes. This makes it possible to analyze the metadata of a VOD service and extract scenes based on detailed content information.

[0043] The system can be integrated with the user interface of a VOD service to provide a function that allows digest videos to be played seamlessly while watching. The system can, for example, be integrated with the user interface of a VOD service to provide a function that allows digest videos to be played seamlessly while watching. For example, a button can be installed that allows digest videos to be played with one click while watching. The system can also analyze user preferences based on the user interface and provide appropriate scenes. For example, scenes can be selected based on viewing history and rating data. The system can also be built to be integrated with the user interface. For example, a generation AI can analyze the user interface and select and provide appropriate scenes. This makes it possible to integrate with the user interface of a VOD service to provide a function that allows digest videos to be played seamlessly while watching.

[0044] The system can realize content sharing between different VOD services and enable digest videos to be viewed on multiple platforms. The system, for example, can realize content sharing between different VOD services and enable digest videos to be viewed on multiple platforms. For example, the system can integrate content from different VOD services and generate digest videos. The system can also analyze user preferences based on content sharing and provide appropriate scenes. For example, scenes can be selected based on viewing history and rating data. The system can also build a system that realizes content sharing. For example, a generation AI analyzes data from different VOD services and selects and provides appropriate scenes. This makes it possible to realize content sharing between different VOD services and enable digest videos to be viewed on multiple platforms.

[0045] The system can work in conjunction with the live streaming function of a VOD service to generate and distribute digest videos in real time. For example, the system can work in conjunction with the live streaming function of a VOD service to generate and distribute digest videos in real time. For example, important scenes can be extracted in real time during a live stream to generate a digest video. The system can also analyze user preferences based on data from the live stream and provide appropriate scenes. For example, scenes can be selected based on viewing history and rating data. The system can also be used to build a system that works in conjunction with the live streaming function. For example, a generation AI can analyze data from a live stream, select appropriate scenes, and distribute them in real time. This makes it possible to work in conjunction with the live streaming function of a VOD service to generate and distribute digest videos in real time.

[0046] The system can periodically evaluate the performance of the digest video generation AI and improve the algorithm based on user feedback. For example, the system can periodically evaluate the performance of the digest video generation AI and improve the algorithm based on user feedback. For example, the system can collect viewer ratings and comments and identify areas for improvement in the algorithm. The system can also improve the algorithm based on periodic evaluations. For example, the system can periodically evaluate performance and update the algorithm based on feedback. The system can also build a system for performance evaluation and feedback collection. For example, the generation AI can analyze viewing data, identify appropriate areas for improvement, and improve the algorithm. This allows the system to periodically evaluate the performance of the digest video generation AI and improve the algorithm based on user feedback.

[0047] The system can work in conjunction with the marketing strategy of a VOD service to utilize digest videos as a promotional tool. For example, the system can work in conjunction with the marketing strategy of a VOD service to utilize digest videos as a promotional tool. For example, digest videos can be used to promote new movies. The system can also analyze user interests based on the marketing strategy and provide appropriate scenes. For example, scenes can be selected based on viewing history and rating data. The system can also build a system that works in conjunction with the marketing strategy. For example, a generative AI analyzes marketing data and selects appropriate scenes to use in promotions. This makes it possible to work in conjunction with the marketing strategy of a VOD service to utilize digest videos as a promotional tool.

[0048] The system can also apply digest video generation AI to other entertainment fields to develop new markets. For example, the system can apply digest video generation AI to the music field to develop new markets. For example, it can generate highlight videos of concerts. The system can also develop new markets by applying it to other entertainment fields. For example, it can generate highlight videos of games. The system can also be used to build systems that can be applied to the entertainment field. For example, the generation AI can analyze music or game data, select appropriate scenes, and generate digest videos. This allows digest video generation AI to be applied to other entertainment fields to develop new markets.

[0049] The system can apply digest video generation AI to the fields of education and business to generate digest videos for presentations and training. For example, the system can apply digest video generation AI to the field of education to generate digest videos for presentations. For example, it can create videos that extract important parts of lectures. The system can also be applied to the business field to generate digest videos for training. For example, it can create videos that extract important parts of corporate training. The system can also be used to build systems that can be applied to the fields of education and business. For example, the generation AI can analyze education and business data, select appropriate scenes, and generate digest videos. This allows digest video generation AI to be applied to the fields of education and business to generate digest videos for presentations and training.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The system can analyze a user's viewing history and generate personalized digest videos based on their viewing habits. For example, a user who watches a lot of action movies can be provided with videos containing many action scenes. The system can also analyze a user's preferences based on the viewing history and provide personalized digest videos. For example, the system can identify a user's favorite genre from the viewing history and provide videos containing many scenes from that genre. The system can also build a system that analyzes viewing history. For example, a generation AI can analyze the viewing history and select appropriate scenes for personalization. This makes it possible to generate personalized digest videos based on the user's viewing history.

[0052] The system can analyze a user's social media activity and generate personalized digest videos based on their interests. For example, the system can analyze a user's social media activity, such as posts and likes, and generate personalized digest videos based on their interests. For example, it can provide videos that include many scenes related to a specific topic. The system can also analyze a user's interests based on social media activity and provide personalized digest videos. For example, it can select scenes based on the accounts the user follows or the groups the user joins. The system can also build a system that analyzes social media activity. For example, a generation AI can analyze social media data and select appropriate scenes for personalization. This makes it possible to generate personalized digest videos based on a user's social media activity.

[0053] The system can provide digest videos containing region-specific content based on the user's geographical location information. For example, the system can analyze the user's geographical location information and provide digest videos containing region-specific content. For example, the system can provide videos containing scenes related to local news and events. The system can also analyze the user's interests based on the geographical location information and provide region-specific content. For example, the system can provide videos containing scenes related to local culture and tourist spots. The system can also build a system that analyzes geographical location information. For example, a generation AI can analyze GPS data and IP addresses and select and provide appropriate scenes. This makes it possible to provide digest videos containing region-specific content based on the user's geographical location information.

[0054] The system can analyze the metadata of a VOD service and extract scenes based on detailed content information. For example, the system can analyze the metadata of a VOD service and extract scenes based on detailed content information. For example, it can identify important scenes based on cast and staff information. The system can also analyze user preferences based on the metadata and extract appropriate scenes. For example, it can select scenes based on viewing history and rating data. The system can also build a system that analyzes metadata. For example, a generation AI can analyze tag information and attribute data to select and extract appropriate scenes. This makes it possible to analyze the metadata of a VOD service and extract scenes based on detailed content information.

[0055] The system can be integrated with the user interface of a VOD service to provide a function that allows digest videos to be played seamlessly while watching. For example, the system can be integrated with the user interface of a VOD service to provide a function that allows digest videos to be played seamlessly while watching. For example, a button can be installed to play digest videos with one click while watching. The system can also analyze user preferences based on the user interface and provide appropriate scenes. For example, scenes can be selected based on viewing history and rating data. The system can also be built to be integrated with the user interface. For example, a generation AI can analyze the user interface and select and provide appropriate scenes. This makes it possible to integrate with the user interface of a VOD service to provide a function that allows digest videos to be played seamlessly while watching.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The video analysis unit analyzes the video content. For example, it performs frame analysis to detect objects in the video and motion analysis to analyze the movement within the video. It can also analyze audio data to detect specific keywords or phrases. It uses voice recognition technology to convert the audio data into text data and extract specific keywords or phrases. Step 2: The scene extraction unit extracts specific scenes from the video content analyzed by the video analysis unit. For example, it extracts scenes based on important events or emotional moments, and identifies scenes with visual impact. It also simultaneously analyzes footage from different camera angles and viewpoints to extract the most compelling scenes. Step 3: The digest generator generates a digest video based on the scenes extracted by the scene extractor. For example, it arranges the extracted scenes in order and edits them to make the story flow clear. It can also automatically add music and sound effects to enhance the viewing experience. It can also adjust the video speed and add effects such as playing important scenes in slow motion.

[0058] (Example 2) The digest video generation AI system according to an embodiment of the present invention is a system that can be used when a viewer wants to watch a specific scene or cannot watch the entire video due to time constraints. This system is realized by combining generative AI with conventional AI. As a result, the digest video generation AI system can generate digest videos that meet the viewer's needs and personalize the viewing experience.

[0059] The digest video generation AI system according to the embodiment includes a video analysis unit, a scene extraction unit, and a digest generation unit. The video analysis unit analyzes video content. For example, the video analysis unit performs frame analysis to detect objects within the video. The video analysis unit can also perform motion analysis to analyze movement within the video. The video analysis unit can also analyze audio data to detect specific keywords or phrases. For example, the video analysis unit uses voice recognition technology to convert audio data within the video into text data and extract specific keywords or phrases. The scene extraction unit extracts specific scenes from the video content analyzed by the video analysis unit. For example, the scene extraction unit extracts scenes based on important events or emotional moments. The scene extraction unit can also identify scenes with visual impact. The scene extraction unit can also simultaneously analyze video from different camera angles or viewpoints to extract the most appealing scenes. For example, the scene extraction unit compares video from multiple camera angles and extracts the most visually impactful scenes. The digest generation unit generates a digest video based on the scenes extracted by the scene extraction unit. For example, the digest generation unit arranges the extracted scenes in order and edits them so that the flow of the story can be understood. The digest generation unit can also automatically add music and sound effects to improve the viewing experience. The digest generation unit can also add effects such as adjusting the speed of the video and playing important scenes in slow motion. For example, the digest generation unit can play important scenes in slow motion to emphasize their visual impact. In this way, the digest video generation AI system according to the embodiment can generate digest videos that meet the viewer's needs and personalize the viewing experience. For example, if a user wants to watch only the climax scene of a movie or only the highlight scenes of a sports program, the system can be used to watch important scenes in a short amount of time. The viewing experience can also be improved by providing digest videos personalized to the user's preferences.

[0060] The video analysis unit can automatically detect emotional moments and extract scenes based on the user's emotions. The video analysis unit, for example, uses facial expression recognition technology to analyze the emotions of characters. For example, it detects facial expressions such as smiles and tears and extracts moving scenes. The video analysis unit can also estimate emotions using audio analysis technology. For example, it can analyze the tone and speed of voice and calculate an emotion score. The video analysis unit can also analyze the context of a scene to identify emotional moments. For example, it can analyze the context before and after a scene to identify the emotional climax. This makes it possible to extract scenes based on the user's emotions.

[0061] The video analysis unit can analyze audio data and extract scenes based on specific keywords or phrases. The video analysis unit, for example, converts audio data into text data using speech recognition technology. For example, speech recognition software automatically analyzes audio and saves it as text. The video analysis unit can also use natural language processing technology to extract specific keywords or phrases. For example, it can identify scenes based on frequently occurring words or important words. The video analysis unit can also perform acoustic feature analysis to detect specific phrases. For example, it can analyze the pitch and rhythm of audio to extract specific phrases. This makes it possible to extract scenes based on audio data.

[0062] The video analysis unit can analyze visual features and identify scenes that have a visual impact. The video analysis unit can, for example, analyze changes in color and identify scenes that have a visual impact. For example, it can extract scenes in which colors change suddenly. The video analysis unit can also analyze changes in shape and identify scenes that have a visual impact. For example, it can extract scenes in which shapes change suddenly. The video analysis unit can also analyze changes in movement and identify scenes that have a visual impact. For example, it can extract scenes in which movements change suddenly. In this way, it is possible to identify scenes that have a visual impact.

[0063] The scene extraction unit can simultaneously analyze video from different camera angles or viewpoints and extract the most attractive scene. For example, the scene extraction unit can simultaneously analyze video from multiple camera angles and identify the most attractive scene. For example, the scene extraction unit can compare video from multiple camera angles and extract the scene with the most visual impact. The scene extraction unit can also simultaneously analyze video from different viewpoints and identify the most attractive scene. For example, the scene extraction unit can compare video from a subjective viewpoint and an objective viewpoint and extract the most attractive scene. The scene extraction unit can also integrate video from different camera angles or viewpoints and identify the most attractive scene. For example, the scene extraction unit can integrate video from multiple camera angles and extract the scene with the most visual impact. This makes it possible to analyze video from different camera angles or viewpoints and extract the most attractive scene.

[0064] The scene extraction unit can analyze the facial expressions and movements of the characters and extract scenes that focus on a specific character. The scene extraction unit, for example, analyzes the facial expressions of the characters and extracts scenes that focus on a specific character. For example, scenes are identified based on facial expressions such as smiling or crying. The scene extraction unit can also analyze the movements of the characters and extract scenes that focus on a specific character. For example, scenes are identified based on movements such as walking or jumping. The scene extraction unit can also combine the facial expressions and movements of the characters to extract scenes that focus on a specific character. For example, a scene is identified based on a combination of smiling and walking. This makes it possible to extract scenes that focus on a specific character.

[0065] The scene extraction unit can use the emotion estimation function to monitor the emotions felt by the user while watching in real time and extract scenes based on the emotions. The scene extraction unit, for example, uses the emotion estimation function to monitor the emotions felt by the user while watching in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The scene extraction unit can also extract scenes based on the emotions monitored in real time. For example, it identifies scenes with high emotion scores. The scene extraction unit can also build a system that uses the emotion estimation function to monitor the user's emotions in real time and extracts scenes based on the emotions. For example, it calculates emotion scores in real time and identifies scenes. This makes it possible to extract scenes based on the user's emotions in real time.

[0066] The digest generation unit can automatically add music and sound effects when generating a digest video to improve the viewing experience. The digest generation unit, for example, uses a generation AI to automatically add music and sound effects suitable for the digest video. For example, music that matches the atmosphere of a scene is selected and inserted into the video. The digest generation unit can also improve the viewing experience by adding sound effects. For example, sound effects can be added to action scenes to emphasize visual impact. The digest generation unit can also build a system that automatically adds music and sound effects. For example, the generation AI analyzes the atmosphere of a scene and selects appropriate music and sound effects. This makes it possible to improve the viewing experience by adding music and sound effects.

[0067] The digest generation unit can adjust the speed of the video when generating the digest video and add effects such as playing important scenes in slow motion. The digest generation unit can, for example, use generation AI to adjust the speed of the video when generating the digest video. For example, important scenes can be played in slow motion to emphasize their visual impact. The digest generation unit can also improve the viewing experience by adjusting the speed of the video. For example, action scenes can be played in slow motion to emphasize their visual impact. The digest generation unit can also build a system that adjusts the speed of the video. For example, the generation AI can analyze the importance of a scene and play it at an appropriate speed. This allows the speed of the video to be adjusted and important scenes to be emphasized.

[0068] The digest generation unit can generate cross-genre digest videos that combine video content of different genres. The digest generation unit generates, for example, cross-genre digest videos that combine video content of different genres. For example, it creates a video that combines highlights of a movie and a sports program. The digest generation unit can also improve the viewing experience by combining video content of different genres. For example, it creates a video that combines highlights of news and documentaries. The digest generation unit can also build a system that combines video content of different genres. For example, a generation AI analyzes videos of different genres and selects and combines appropriate scenes. This makes it possible to generate digest videos that combine video content of different genres.

[0069] The digest generation unit can add interactive elements to the digest video, allowing the user to select and view scenes. The digest generation unit can, for example, add interactive elements to the digest video, allowing the user to select and view scenes. For example, a selection button can be provided for each scene, allowing the user to select scenes of interest. The digest generation unit can also add an interactive menu, allowing the user to select and view scenes. For example, thumbnails of scenes can be displayed, allowing the user to click to view them. The digest generation unit can also build a system that adds interactive elements. For example, a generation AI can analyze the importance of scenes and add appropriate interactive elements. This makes it possible to provide an interactive digest video that allows the user to select and view scenes.

[0070] The digest generation unit can use the emotion estimation function to generate a digest video that emphasizes the scenes that move the user most. For example, the digest generation unit uses the emotion estimation function to identify the scenes that move the user most and generate a digest video that emphasizes those scenes. For example, the digest generation unit can edit the video to focus on moving scenes. The digest generation unit can also use the emotion estimation function to analyze the user's emotions and emphasize scenes based on those emotions. For example, it can identify scenes with high emotion scores and emphasize those scenes. The digest generation unit can also use the emotion estimation function to build a system that emphasizes the scenes that move the user most. For example, a generation AI analyzes the emotion scores and selects and emphasizes appropriate scenes. This makes it possible to generate a digest video that emphasizes the scenes that move the user most.

[0071] The system can analyze a user's viewing history and generate a personalized digest video based on their viewing habits. For example, the system can analyze a user's viewing history and generate a personalized digest video based on their viewing habits. For example, a user who watches a lot of action movies can be provided with videos containing many action scenes. The system can also analyze a user's preferences based on the viewing history and provide a personalized digest video. For example, the system can identify a user's favorite genre from the viewing history and provide videos containing many scenes of that genre. The system can also build a system that analyzes viewing history. For example, a generation AI can analyze the viewing history and select appropriate scenes for personalization. This makes it possible to generate a personalized digest video based on the user's viewing history.

[0072] The system can provide digest videos with optimal image quality and playback speed depending on the user's viewing environment. For example, the system can analyze the user's device and network conditions and provide digest videos with optimal image quality. For example, in a slow network environment, it can provide videos with lower image quality. The system can also adjust the playback speed depending on the user's viewing environment. For example, it can change the playback speed depending on the viewing environment to provide an optimal viewing experience. The system can also build a system that analyzes the viewing environment. For example, a generation AI can analyze the device and network conditions and select appropriate image quality and playback speed. This makes it possible to provide digest videos with optimal image quality and playback speed depending on the user's viewing environment.

[0073] The system can analyze a user's social media activity and generate a personalized digest video based on their interests. For example, the system can analyze a user's social media activity, such as posts and likes, and generate a personalized digest video based on their interests. For example, the system can provide videos that include many scenes related to a specific topic. The system can also analyze a user's interests based on their social media activity and provide a personalized digest video. For example, the system can select scenes based on the accounts the user follows or the groups the user joins. The system can also build a system that analyzes social media activity. For example, a generation AI can analyze social media data and select appropriate scenes for personalization. This makes it possible to generate a personalized digest video based on the user's social media activity.

[0074] The system can provide digest videos containing region-specific content based on the user's geographical location information. For example, the system can analyze the user's geographical location information and provide digest videos containing region-specific content. For example, videos containing scenes related to local news and events can be provided. The system can also analyze the user's interests based on the geographical location information and provide region-specific content. For example, videos containing scenes related to local culture and tourist spots can be provided. The system can also be constructed to analyze geographical location information. For example, a generation AI can analyze GPS data and IP addresses and select and provide appropriate scenes. This makes it possible to provide digest videos containing region-specific content based on the user's geographical location information.

[0075] The system can use the emotion estimation function to generate a personalized digest video based on the emotions felt by the user while watching. For example, the system uses the emotion estimation function to analyze the emotions felt by the user while watching and generates a personalized digest video based on those emotions. For example, the system can provide a video that includes many moving scenes. The system can also analyze the user's preferences based on the emotions felt while watching and provide a personalized digest video. For example, the system can identify scenes with high emotion scores and provide videos that include many of those scenes. The system can also build a system that analyzes the user's emotions using the emotion estimation function. For example, the generation AI calculates emotion scores in real time and selects appropriate scenes for personalization. This makes it possible to generate a personalized digest video based on the user's emotions.

[0076] The system can analyze the metadata of a VOD service and extract scenes based on detailed content information. For example, the system analyzes the metadata of a VOD service and extracts scenes based on detailed content information. For example, important scenes can be identified based on cast and staff information. The system can also analyze user preferences based on the metadata and extract appropriate scenes. For example, scenes can be selected based on viewing history and rating data. The system can also be built to analyze metadata. For example, a generation AI analyzes tag information and attribute data to select and extract appropriate scenes. This makes it possible to analyze the metadata of a VOD service and extract scenes based on detailed content information.

[0077] The system can be integrated with the user interface of a VOD service to provide a function that allows digest videos to be played seamlessly while watching. The system can, for example, be integrated with the user interface of a VOD service to provide a function that allows digest videos to be played seamlessly while watching. For example, a button can be installed that allows digest videos to be played with one click while watching. The system can also analyze user preferences based on the user interface and provide appropriate scenes. For example, scenes can be selected based on viewing history and rating data. The system can also be built to be integrated with the user interface. For example, a generation AI can analyze the user interface and select and provide appropriate scenes. This makes it possible to integrate with the user interface of a VOD service to provide a function that allows digest videos to be played seamlessly while watching.

[0078] The system can realize content sharing between different VOD services and enable digest videos to be viewed on multiple platforms. The system, for example, can realize content sharing between different VOD services and enable digest videos to be viewed on multiple platforms. For example, the system can integrate content from different VOD services and generate digest videos. The system can also analyze user preferences based on content sharing and provide appropriate scenes. For example, scenes can be selected based on viewing history and rating data. The system can also build a system that realizes content sharing. For example, a generation AI analyzes data from different VOD services and selects and provides appropriate scenes. This makes it possible to realize content sharing between different VOD services and enable digest videos to be viewed on multiple platforms.

[0079] The system can work in conjunction with the live streaming function of a VOD service to generate and distribute digest videos in real time. For example, the system can work in conjunction with the live streaming function of a VOD service to generate and distribute digest videos in real time. For example, important scenes can be extracted in real time during a live stream to generate a digest video. The system can also analyze user preferences based on data from the live stream and provide appropriate scenes. For example, scenes can be selected based on viewing history and rating data. The system can also be used to build a system that works in conjunction with the live streaming function. For example, a generation AI can analyze data from a live stream, select appropriate scenes, and distribute them in real time. This makes it possible to work in conjunction with the live streaming function of a VOD service to generate and distribute digest videos in real time.

[0080] The system can use the emotion estimation function to identify scenes that VOD service users are most interested in and provide digest videos that include those scenes. For example, the system can use the emotion estimation function to identify scenes that VOD service users are most interested in and provide digest videos that include those scenes. For example, the system can edit the videos to focus on scenes with high emotion scores. The system can also identify scenes that users are interested in based on their viewing history and behavioral data and provide appropriate scenes. For example, the system can select scenes based on their viewing history and rating data. The system can also build a system that analyzes user interests using the emotion estimation function. For example, a generation AI can analyze emotion scores and select and provide appropriate scenes. This makes it possible to identify scenes that VOD service users are most interested in and provide digest videos that include those scenes.

[0081] The system can periodically evaluate the performance of the digest video generation AI and improve the algorithm based on user feedback. For example, the system can periodically evaluate the performance of the digest video generation AI and improve the algorithm based on user feedback. For example, the system can collect viewer ratings and comments and identify areas for improvement in the algorithm. The system can also improve the algorithm based on periodic evaluations. For example, the system can periodically evaluate performance and update the algorithm based on feedback. The system can also build a system for performance evaluation and feedback collection. For example, the generation AI can analyze viewing data, identify appropriate areas for improvement, and improve the algorithm. This allows the system to periodically evaluate the performance of the digest video generation AI and improve the algorithm based on user feedback.

[0082] The system can work in conjunction with the marketing strategy of a VOD service to utilize digest videos as a promotional tool. For example, the system can work in conjunction with the marketing strategy of a VOD service to utilize digest videos as a promotional tool. For example, digest videos can be used to promote new movies. The system can also analyze user interests based on the marketing strategy and provide appropriate scenes. For example, scenes can be selected based on viewing history and rating data. The system can also build a system that works in conjunction with the marketing strategy. For example, a generative AI analyzes marketing data and selects appropriate scenes to use in promotions. This makes it possible to work in conjunction with the marketing strategy of a VOD service to utilize digest videos as a promotional tool.

[0083] The system can also apply digest video generation AI to other entertainment fields to develop new markets. For example, the system can apply digest video generation AI to the music field to develop new markets. For example, it can generate highlight videos of concerts. The system can also develop new markets by applying it to other entertainment fields. For example, it can generate highlight videos of games. The system can also be used to build systems that can be applied to the entertainment field. For example, the generation AI can analyze music or game data, select appropriate scenes, and generate digest videos. This allows digest video generation AI to be applied to other entertainment fields to develop new markets.

[0084] The system can apply digest video generation AI to the fields of education and business to generate digest videos for presentations and training. For example, the system can apply digest video generation AI to the field of education to generate digest videos for presentations. For example, it can create videos that extract important parts of lectures. The system can also be applied to the business field to generate digest videos for training. For example, it can create videos that extract important parts of corporate training. The system can also be used to build systems that can be applied to the fields of education and business. For example, the generation AI can analyze education and business data, select appropriate scenes, and generate digest videos. This allows digest video generation AI to be applied to the fields of education and business to generate digest videos for presentations and training.

[0085] The system uses the emotion estimation function to generate digest videos that highlight the scenes that move the user most, thereby differentiating VOD services. For example, the system uses the emotion estimation function to identify the scenes that move the user most and generate digest videos that highlight those scenes. For example, the system edits the video to focus on the most moving scenes. The system can also use the emotion estimation function to analyze the user's emotions and highlight scenes based on those emotions. For example, it can identify scenes with high emotion scores and highlight those scenes. The system can also use the emotion estimation function to build a system that highlights the scenes that move the user most. For example, the generation AI analyzes the emotion scores and selects and highlights appropriate scenes. This allows the system to generate digest videos that highlight the scenes that move the user most using the emotion estimation function, thereby differentiating VOD services.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The system can analyze a user's viewing history and generate personalized digest videos based on their viewing habits. For example, a user who watches a lot of action movies can be provided with videos containing many action scenes. The system can also analyze a user's preferences based on the viewing history and provide personalized digest videos. For example, the system can identify a user's favorite genre from the viewing history and provide videos containing many scenes from that genre. The system can also build a system that analyzes viewing history. For example, a generation AI can analyze the viewing history and select appropriate scenes for personalization. This makes it possible to generate personalized digest videos based on the user's viewing history.

[0088] The system can analyze a user's social media activity and generate personalized digest videos based on their interests. For example, the system can analyze a user's social media activity, such as posts and likes, and generate personalized digest videos based on their interests. For example, it can provide videos that include many scenes related to a specific topic. The system can also analyze a user's interests based on social media activity and provide personalized digest videos. For example, it can select scenes based on the accounts the user follows or the groups the user joins. The system can also build a system that analyzes social media activity. For example, a generation AI can analyze social media data and select appropriate scenes for personalization. This makes it possible to generate personalized digest videos based on a user's social media activity.

[0089] The system can provide digest videos containing region-specific content based on the user's geographical location information. For example, the system can analyze the user's geographical location information and provide digest videos containing region-specific content. For example, the system can provide videos containing scenes related to local news and events. The system can also analyze the user's interests based on the geographical location information and provide region-specific content. For example, the system can provide videos containing scenes related to local culture and tourist spots. The system can also build a system that analyzes geographical location information. For example, a generation AI can analyze GPS data and IP addresses and select and provide appropriate scenes. This makes it possible to provide digest videos containing region-specific content based on the user's geographical location information.

[0090] The system can analyze the metadata of a VOD service and extract scenes based on detailed content information. For example, the system can analyze the metadata of a VOD service and extract scenes based on detailed content information. For example, it can identify important scenes based on cast and staff information. The system can also analyze user preferences based on the metadata and extract appropriate scenes. For example, it can select scenes based on viewing history and rating data. The system can also build a system that analyzes metadata. For example, a generation AI can analyze tag information and attribute data to select and extract appropriate scenes. This makes it possible to analyze the metadata of a VOD service and extract scenes based on detailed content information.

[0091] The system can be integrated with the user interface of a VOD service to provide a function that allows digest videos to be played seamlessly while watching. For example, the system can be integrated with the user interface of a VOD service to provide a function that allows digest videos to be played seamlessly while watching. For example, a button can be installed to play digest videos with one click while watching. The system can also analyze user preferences based on the user interface and provide appropriate scenes. For example, scenes can be selected based on viewing history and rating data. The system can also be built to be integrated with the user interface. For example, a generation AI can analyze the user interface and select and provide appropriate scenes. This makes it possible to integrate with the user interface of a VOD service to provide a function that allows digest videos to be played seamlessly while watching.

[0092] The system can use the emotion estimation function to generate personalized digest videos based on the emotions felt by the user while watching. For example, the emotion estimation function can be used to analyze the emotions felt by the user while watching, and a personalized digest video can be generated based on those emotions. For example, videos containing many moving scenes can be provided. The system can also analyze the user's preferences based on the emotions felt while watching, and provide personalized digest videos. For example, scenes with high emotion scores can be identified, and videos containing many of those scenes can be provided. The system can also build a system that analyzes user emotions using the emotion estimation function. For example, a generation AI can calculate emotion scores in real time, select appropriate scenes, and personalize the video. This makes it possible to generate personalized digest videos based on the user's emotions.

[0093] The system can use the emotion estimation function to generate a digest video that emphasizes the scenes that move the user the most. For example, the system can use the emotion estimation function to identify the scenes that move the user the most and generate a digest video that emphasizes those scenes. For example, the system can edit the video to focus on the most moving scenes. The system can also use the emotion estimation function to analyze the user's emotions and emphasize scenes based on those emotions. For example, the system can identify scenes with high emotion scores and emphasize those scenes. The system can also use the emotion estimation function to build a system that emphasizes the scenes that move the user the most. For example, the generation AI analyzes the emotion scores and selects and emphasizes appropriate scenes. This makes it possible to generate a digest video that emphasizes the scenes that move the user the most.

[0094] The system can use the emotion estimation function to identify scenes that VOD service users are most interested in and provide digest videos that include those scenes. For example, the system can use the emotion estimation function to identify scenes that VOD service users are most interested in and provide digest videos that include those scenes. For example, the system can edit the videos to focus on scenes with high emotion scores. The system can also identify scenes that users are interested in based on their viewing history and behavioral data and provide appropriate scenes. For example, the system can select scenes based on their viewing history and rating data. The system can also build a system that analyzes user interests using the emotion estimation function. For example, a generation AI can analyze emotion scores and select and provide appropriate scenes. This makes it possible to identify scenes that VOD service users are most interested in and provide digest videos that include those scenes.

[0095] The system can use the emotion estimation function to monitor the emotions felt by a user while watching in real time, and extract scenes based on those emotions. For example, the emotion estimation function can be used to monitor the emotions felt by a user while watching in real time. For example, the user's facial expressions and voice can be analyzed to calculate an emotion score. The system can also extract scenes based on the emotions monitored in real time. For example, scenes with high emotion scores can be identified. The system can also be used to monitor the emotions of a user in real time, and extract scenes based on those emotions. For example, emotion scores can be calculated in real time to identify scenes. This makes it possible to extract scenes based on the user's emotions in real time.

[0096] The system can use the emotion estimation function to generate personalized digest videos based on the emotions felt by the user while watching. For example, the emotion estimation function can be used to analyze the emotions felt by the user while watching, and a personalized digest video can be generated based on those emotions. For example, videos containing many moving scenes can be provided. The system can also analyze the user's preferences based on the emotions felt while watching, and provide personalized digest videos. For example, scenes with high emotion scores can be identified, and videos containing many of those scenes can be provided. The system can also build a system that analyzes user emotions using the emotion estimation function. For example, a generation AI can calculate emotion scores in real time, select appropriate scenes, and personalize the video. This makes it possible to generate personalized digest videos based on the user's emotions.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The video analysis unit analyzes the video content. For example, it performs frame analysis to detect objects in the video and motion analysis to analyze the movement within the video. It can also analyze audio data to detect specific keywords or phrases. It uses voice recognition technology to convert the audio data into text data and extract specific keywords or phrases. Step 2: The scene extraction unit extracts specific scenes from the video content analyzed by the video analysis unit. For example, it extracts scenes based on important events or emotional moments, and identifies scenes with visual impact. It also simultaneously analyzes footage from different camera angles and viewpoints to extract the most compelling scenes. Step 3: The digest generator generates a digest video based on the scenes extracted by the scene extractor. For example, it arranges the extracted scenes in order and edits them to make the story flow clear. It can also automatically add music and sound effects to enhance the viewing experience. It can also adjust the video speed and add effects such as playing important scenes in slow motion.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a video analysis unit that analyzes video content; a scene extraction unit that extracts a specific scene from the video content analyzed by the video analysis unit; a digest generation unit that generates a digest video based on the scenes extracted by the scene extraction unit. A system characterized by:

2. The video analysis unit Automatically detect emotional moments in video and extract scenes based on user emotions 2. The system of claim 1.

3. The scene extraction unit Simultaneously analyze footage from different camera angles and perspectives to extract the most compelling scenes 2. The system of claim 1.

4. The digest generation unit When generating the digest video, the video speed is adjusted and effects such as playing important scenes in slow motion are added.

2. The system of claim 1.

5. The system comprises: Analyzing the user's viewing history and generating the personalized digest video based on the viewing habits 2. The system of claim 1.

6. The system comprises: The metadata of the VOD service is analyzed, and the scene is extracted based on detailed information of the content.

2. The system of claim 1.

7. The system comprises: Regularly evaluate the performance of the digest video generation AI and improve the algorithm based on user feedback.

2. The system of claim 1.

8. The system comprises: The digest video is generated by emphasizing the scenes that most impress the user, thereby differentiating the VOD service.

2. The system of claim 1.

Images