system

Abstract

The system according to the embodiment aims to enable efficient viewing of only the interesting or important parts of a video. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and an editing unit. The reception unit receives input of a video that a user wishes to watch. The analysis unit analyzes the video received by the reception unit and identifies specific scenes or events. The editing unit edits the portions identified by the analysis unit to enable the user to watch the video efficiently.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to efficiently watch only the interesting or important parts of a video.

[0005] The system according to the embodiment aims to enable efficient viewing of only the interesting or important parts of a video. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and an editing unit. The reception unit receives input of a video that a user wishes to watch. The analysis unit analyzes the video received by the reception unit and identifies specific scenes or events. The editing unit edits the portions identified by the analysis unit to enable the user to watch the video efficiently. [Effects of the Invention]

[0007] The system according to the embodiment can enable users to efficiently watch only the interesting or important parts of a video. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A video viewing support system according to an embodiment of the present invention allows a user to efficiently view only the interesting or important parts of a video. In the video viewing support system, a user selects a video they want to view, and AI analyzes the entire video and automatically extracts the interesting or important parts. The extracted parts are edited and played back for efficient viewing by the user. For example, the video viewing support system allows a user to select a video by inputting the URL of the video they want to view or by uploading a video file. Next, the video viewing support system analyzes the entire video using AI. The AI ​​converts audio data in the video into text using speech recognition technology and identifies important keywords and phrases. The video viewing support system also analyzes video data in the video using image recognition technology to detect specific scenes and events. Furthermore, the video viewing support system analyzes text extracted from the audio data using natural language processing to understand the content of the video. Using machine learning, the system identifies the interesting or important parts using a model learned from past data. Next, the video viewing support system edits the extracted parts for efficient viewing by the user. For example, the system edits the video so that only the interesting or important parts are played continuously. This allows users to watch long videos in a short amount of time. Users can also skip extracted sections or adjust the playback speed. This allows the video viewing support system to grasp important information without missing it. This saves users time and provides important information effectively. For example, in a lecture video, users can efficiently learn important points, and in a movie or drama, users can enjoy moving scenes without missing them. In a business presentation video, users can efficiently check important proposals and conclusions.

[0029] A video viewing support system according to an embodiment includes a reception unit, an analysis unit, and an editing unit. The reception unit receives input of a video that a user wishes to view. Examples of videos that a user wishes to view include, but are not limited to, movies, television programs, and online videos. The reception unit selects a video by, for example, inputting a video URL or uploading a video file. The analysis unit analyzes the video received by the reception unit and identifies specific scenes and events. The analysis unit, for example, converts audio data in the video into text using speech recognition technology and identifies important keywords and phrases. The analysis unit can also analyze video data in the video using image recognition technology and detect specific scenes and events. The analysis unit can also analyze text extracted from audio data using natural language processing to understand the content of the video. The analysis unit uses machine learning to identify interesting or important parts using a model learned from past data. For example, the analysis unit converts audio data in the video into text using speech recognition technology and identifies important keywords and phrases. The analysis unit can also analyze video data in the video using image recognition technology and detect specific scenes and events. Furthermore, the analysis unit can analyze text extracted from audio data using natural language processing to understand the content of the video. The analysis unit uses machine learning to identify interesting or important parts using a model learned from past data. The editing unit edits the parts identified by the analysis unit to enable the user to watch the video efficiently. For example, the editing unit edits the video so that only specific scenes or events are played continuously. The editing unit can also allow the user to skip the extracted parts or adjust the playback speed. This allows the video viewing support system according to the embodiment to efficiently watch only the interesting or important parts of the video that the user wants to watch. Some or all of the above-mentioned processing in the editing unit may be performed using, for example, AI, or may be performed without using AI. For example, the editing unit can perform editing using an AI model that inputs the parts identified by the analysis unit and outputs editing results.

[0030] The reception unit can select a video by inputting a video URL or uploading a video file. Examples of video URLs include, but are not limited to, protocols such as HTTP, HTTPS, and FTP. Examples of video files include, but are not limited to, formats such as MP4, AVI, and MKV. The reception unit can select a video by, for example, a user inputting a video URL. The reception unit can also select a video by, for example, a user uploading a video file. This allows a user to select a video by inputting a video URL or uploading a video file. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the URL of a video input by the user to a generation AI and cause the generation AI to select a video.

[0031] The analysis unit can convert audio data in a video into text using speech recognition technology and identify important keywords and phrases. Examples of speech recognition technology include, but are not limited to, speech recognition algorithms and speech data preprocessing methods. Examples of important keywords and phrases include, but are not limited to, criteria such as frequency and context dependency. The analysis unit can convert audio data in a video into text using speech recognition technology. The analysis unit can also identify important keywords and phrases from the text data. Thus, important keywords and phrases in a video can be identified using speech recognition technology. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input audio data into a generation AI and have the generation AI convert the audio data into text data and identify important keywords and phrases.

[0032] The analysis unit can analyze video data in a video using image recognition technology to detect specific scenes or events. Image recognition technology includes, but is not limited to, object detection algorithms and image data preprocessing methods. Specific scenes or events include, but are not limited to, criteria such as action scenes, moving scenes, and important events. The analysis unit can also analyze video data in a video using image recognition technology. The analysis unit can also detect specific scenes or events. Thus, specific scenes or events in a video can be detected using image recognition technology. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input video data to a generation AI and cause the generation AI to detect specific scenes or events.

[0033] The analysis unit can analyze text extracted from audio data using natural language processing to understand the content of the video. Natural language processing includes, but is not limited to, techniques such as morphological analysis, grammatical analysis, and semantic analysis. Understanding the content of the video can include, but is not limited to, criteria such as summarizing the text and extracting important information. The analysis unit can analyze the text extracted from audio data using, for example, natural language processing. The analysis unit can also understand the content of the video based on the text data. This allows the content of the video to be understood using natural language processing. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input text data to a generation AI and have the generation AI understand the content of the video.

[0034] The analysis unit can identify interesting and important parts using a model learned from past data through machine learning. Machine learning includes, but is not limited to, techniques such as supervised learning, unsupervised learning, and reinforcement learning. Interesting and important parts can include, but are not limited to, criteria such as viewer reactions and number of views. The analysis unit can identify interesting and important parts using, for example, a model learned from past data through machine learning. In this way, interesting and important parts can be identified using machine learning. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input past data into a generation AI and cause the generation AI to identify interesting and important parts.

[0035] The editing unit can edit only specific scenes or events to be played continuously. Specific scenes or events include, but are not limited to, action scenes, moving scenes, and important events. The editing unit, for example, edits only specific scenes or events to be played continuously. This allows users to watch efficiently by editing only interesting or important parts to be played continuously. Some or all of the above-described processing in the editing unit may be performed using, for example, AI, or may be performed without using AI. For example, the editing unit can input specific scenes or events into a generation AI and have the generation AI edit the scenes or events for continuous playback.

[0036] The editing unit allows the user to skip the extracted portion or adjust the playback speed. The extracted portion may be determined based on criteria such as, but not limited to, importance and viewer interest. The playback speed may be determined based on, but not limited to, 1.5x speed or 2x speed. The editing unit may allow the user to skip the extracted portion. The editing unit may also allow the user to adjust the playback speed. This allows the user to skip the extracted portion or adjust the playback speed. Some or all of the above-described processing in the editing unit may be performed using, or without, AI. For example, the editing unit may input the extracted portion into a generation AI and have the generation AI skip or adjust the playback speed.

[0037] The reception unit can analyze the user's past viewing history and suggest an optimal video selection method. For example, the reception unit can automatically display related videos as candidates based on the genre of videos the user has previously viewed. The reception unit can also prioritize suggesting selection methods (audio, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest videos that the user will watch during a specific time period based on the user's past viewing history. In this way, the optimal video selection method can be suggested by analyzing the user's past viewing history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past viewing history data into a generation AI and cause the generation AI to suggest an optimal video selection method.

[0038] The reception unit can filter videos based on the user's current areas of interest when receiving the videos. For example, the reception unit can preferentially display videos related to topics in which the user is currently interested. The reception unit can also filter related videos based on keywords recently searched by the user. Furthermore, the reception unit can analyze the user's social media activity and suggest videos based on the user's current areas of interest. This makes it possible to provide highly relevant videos by filtering based on the user's current areas of interest. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's interest data into a generation AI and have the generation AI perform the filtering.

[0039] When accepting a video, the acceptance unit can select the optimal acceptance means according to the user's input method. For example, when a user inputs a video URL by voice, the acceptance unit automatically recognizes the URL using voice recognition technology. Furthermore, when a user inputs a video title by text, the acceptance unit can search for and display related videos. Furthermore, when a user uploads an image, the acceptance unit can search for related videos using image recognition technology. This allows for the efficient acceptance of videos by selecting the optimal acceptance means according to the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's input data into a generation AI and have the generation AI select the optimal acceptance means.

[0040] When accepting videos, the reception unit can prioritize accepting highly relevant videos by taking into account the user's geographical location information. For example, the reception unit can prioritize displaying videos of tourist spots related to the user's current location. Furthermore, if the user is in a specific area, the reception unit can also prioritize displaying videos related to news and events in that area. Furthermore, if the user is traveling, the reception unit can also prioritize displaying videos related to the user's travel destination. In this way, highly relevant videos can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data into the generation AI and cause the generation AI to prioritize accepting highly relevant videos.

[0041] When receiving a video, the reception unit can analyze the user's social media activity and suggest related videos. The reception unit can, for example, suggest content related to videos shared by the user on social media. The reception unit can also analyze the content posted by the user on social media and suggest related videos. Furthermore, the reception unit can also suggest related videos by referring to the activity of the user's friends on social media. In this way, related videos can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to suggest related videos.

[0042] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a video. For example, the reception unit can preferentially display genres of videos that the user has previously rated highly. The reception unit can also exclude genres of videos that the user has previously rated poorly. Furthermore, the reception unit can suggest an optimal reception method based on the user's past feedback. This makes it possible to provide an optimal reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's feedback data into the generation AI and cause the generation AI to customize the reception method.

[0043] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the video. For example, the analysis unit performs a detailed analysis for a video with many important scenes. The analysis unit can also perform a simplified analysis for a video with few important scenes. Furthermore, the analysis unit can identify important scenes according to the genre of the video and adjust the level of detail of the analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the video. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the video. For example, in the case of a movie, the analysis unit applies an algorithm that identifies important scenes based on the development of the story. In addition, in the case of a lecture video, the analysis unit can apply an algorithm that identifies important scenes based on the points emphasized by the lecturer. Furthermore, in the case of a news video, the analysis unit can apply an algorithm that identifies important scenes based on the importance of the news. In this way, by applying different analysis algorithms depending on the category of the video, more accurate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis, for example, based on the analysis results of videos the user has watched in the past. The analysis unit can also adjust the analysis algorithm by referring to the user's past feedback. Furthermore, the analysis unit can analyze the user's past viewing history and improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0046] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the video. For example, the analysis unit prioritizes analysis of the most recent video. The analysis unit can also determine the analysis priority based on a deadline specified by the user. Furthermore, the analysis unit can also determine the analysis priority taking into account the publication date of the video. This allows for efficient analysis by determining the analysis priority based on the time of submission of the video. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video submission time data into the generation AI and have the generation AI determine the analysis priority.

[0047] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the videos. For example, the analysis unit prioritizes analysis of videos related to a video the user wants to watch. The analysis unit can also adjust the order of analysis taking into account the relevance of videos the user has previously watched. Furthermore, the analysis unit can prioritize analysis of highly relevant videos based on the genre or theme of the video. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the videos. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video relevance data into a generation AI and have the generation AI adjust the order of analysis.

[0048] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user is a beginner, the analysis unit can also provide analysis results that avoid technical terminology. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's technical expertise data into a generation AI and have the generation AI adjust the use of technical terminology.

[0049] During editing, the editing department can adjust the level of editing detail based on the importance of the video. For example, the editing department performs detailed editing for a video with many important scenes. The editing department can also perform simplified editing for a video with few important scenes. Furthermore, the editing department can identify important scenes according to the genre of the video and adjust the level of editing detail. This allows for efficient editing by adjusting the level of editing detail based on the importance of the video. Some or all of the above-described processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input video importance data into the generation AI and have the generation AI adjust the level of editing detail.

[0050] During editing, the editing department can apply different editing algorithms depending on the category of the video. For example, in the case of a movie, the editing department applies an algorithm that identifies important scenes based on the development of the story. In addition, in the case of a lecture video, the editing department can apply an algorithm that identifies important scenes based on the points emphasized by the lecturer. Furthermore, in the case of a news video, the editing department can apply an algorithm that identifies important scenes based on the importance of the news. In this way, by applying different editing algorithms depending on the category of the video, more accurate editing can be performed. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input video category data into the generation AI and have the generation AI apply different editing algorithms.

[0051] During editing, the editing department can improve the accuracy of editing by referring to the user's past editing results. The editing department can improve the accuracy of editing, for example, based on the editing results of videos that the user has viewed in the past. The editing department can also adjust the editing algorithm by referring to the user's past feedback. Furthermore, the editing department can analyze the user's past viewing history and improve the accuracy of editing. In this way, the accuracy of editing is improved by referring to the user's past editing results. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input past editing result data into the generation AI and have the generation AI improve the accuracy of editing.

[0052] During editing, the editing department can determine the priority of editing based on the time of submission of the video. For example, the editing department prioritizes editing the most recent video. The editing department can also determine the priority of editing based on a deadline specified by the user. Furthermore, the editing department can determine the priority of editing taking into account the release date of the video. This allows for efficient editing by determining the priority of editing based on the time of submission of the video. Some or all of the above-described processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input video submission time data into the generation AI and have the generation AI determine the priority of editing.

[0053] During editing, the editing department can adjust the editing order based on the relevance of the videos. For example, the editing department prioritizes editing videos related to a video that the user wants to watch. The editing department can also adjust the editing order taking into account the relevance of videos that the user has previously watched. Furthermore, the editing department can also prioritize editing videos that are highly relevant based on the genre or theme of the video. This allows for efficient editing by adjusting the editing order based on the relevance of the videos. Some or all of the above-described processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input video relevance data into a generation AI and have the generation AI adjust the editing order.

[0054] During editing, the editing department can adjust the use of technical terminology in the editing depending on the user's level of expertise. For example, if the user has technical expertise, the editing department can provide an editing result that uses a lot of technical terminology. Also, if the user is a beginner, the editing department can provide an editing result that avoids technical terminology. Furthermore, the editing department can adjust the level of detail of the editing result depending on the user's level of expertise. This allows for more appropriate editing results to be provided by adjusting the use of technical terminology in the editing depending on the user's level of expertise. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input the user's technical expertise data into a generation AI and have the generation AI adjust the use of technical terminology.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The analysis unit can identify scenes and events that a user likes based on the user's viewing history. For example, it can analyze scenes that the user watched for a particularly long time or scenes that have been played many times among videos that the user has watched in the past. It can also identify scenes that the user has given high ratings to or left comments about. It can also analyze scenes that the user stopped watching or skipped, and identify scenes that the user does not like. In this way, the analysis unit can identify scenes and events that the user likes based on the user's viewing history, and provide a more personalized video viewing experience.

[0057] The reception unit can adjust the video selection method based on the user's current activity status. For example, if the user is exercising, voice input can be prioritized to allow hands-free video selection. Alternatively, if the user is working, text input can be prioritized to allow quiet video selection. Furthermore, if the user is relaxing, detailed selection options can be provided and a customizable selection method can be suggested. This allows for more appropriate video selection by adjusting the video selection method based on the user's current activity status.

[0058] The analysis unit can apply different analysis criteria depending on the genre of the video. For example, in the case of a movie, criteria for identifying important scenes based on the development of the story can be applied. In addition, in the case of a lecture video, criteria for identifying important scenes can be applied based on the points emphasized by the lecturer. Furthermore, in the case of a news video, criteria for identifying important scenes can be applied based on the importance of the news. In this way, by applying different analysis criteria depending on the genre of the video, more accurate analysis can be performed.

[0059] The editing department can apply an editing style that a user prefers based on the user's viewing history. For example, the editing department can analyze the editing style that the user has watched for a particularly long time among the videos that the user has watched in the past. It can also identify editing styles that the user has given high ratings to or left comments about. It can also analyze editing styles that the user has stopped watching or skipped, and identify editing styles that the user does not prefer. This allows the editing department to apply an editing style that the user prefers based on the user's viewing history and provide a more personalized video viewing experience.

[0060] The analysis unit can collect video viewing data in real time and dynamically update the analysis results. For example, if a user pauses a particular scene while watching, the importance of that scene can be reevaluated. It can also analyze scenes that the user skips while watching and lower their importance. It can also identify scenes where the user left comments while watching and increase their importance. This allows the analysis unit to perform more accurate analysis by collecting video viewing data in real time and dynamically updating the analysis results.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit accepts input of the video the user wants to watch. The user selects a video by entering the video URL or uploading a video file. The video can be a movie, a TV show, an online video, or anything else. Step 2: The analysis unit analyzes the video received by the reception unit and identifies specific scenes and events. The analysis unit uses speech recognition technology to convert the audio data in the video into text and identify important keywords and phrases. It can also use image recognition technology to analyze the video data and detect specific scenes and events. It then uses natural language processing to analyze the text extracted from the audio data and understand the content of the video. The analysis unit uses machine learning to identify interesting and important parts using a model learned from past data. Step 3: The editing department edits the parts identified by the analysis department to allow users to watch efficiently. The editing department edits the video so that only specific scenes or events are played continuously. Users can also skip the extracted parts or adjust the playback speed. The editing process may or may not be performed using AI.

[0063] (Example 2) A video viewing support system according to an embodiment of the present invention allows a user to efficiently view only the interesting or important parts of a video. In the video viewing support system, a user selects a video they want to view, and AI analyzes the entire video and automatically extracts the interesting or important parts. The extracted parts are edited and played back for efficient viewing by the user. For example, the video viewing support system allows a user to select a video by inputting the URL of the video they want to view or by uploading a video file. Next, the video viewing support system analyzes the entire video using AI. The AI ​​converts audio data in the video into text using speech recognition technology and identifies important keywords and phrases. The video viewing support system also analyzes video data in the video using image recognition technology to detect specific scenes and events. Furthermore, the video viewing support system analyzes text extracted from the audio data using natural language processing to understand the content of the video. Using machine learning, the system identifies the interesting or important parts using a model learned from past data. Next, the video viewing support system edits the extracted parts for efficient viewing by the user. For example, the system edits the video so that only the interesting or important parts are played continuously. This allows users to watch long videos in a short amount of time. Users can also skip extracted sections or adjust the playback speed. This allows the video viewing support system to grasp important information without missing it. This saves users time and provides important information effectively. For example, in a lecture video, users can efficiently learn important points, and in a movie or drama, users can enjoy moving scenes without missing them. In a business presentation video, users can efficiently check important proposals and conclusions.

[0064] A video viewing support system according to an embodiment includes a reception unit, an analysis unit, and an editing unit. The reception unit receives input of a video that a user wishes to view. Examples of videos that a user wishes to view include, but are not limited to, movies, television programs, and online videos. The reception unit selects a video by, for example, inputting a video URL or uploading a video file. The analysis unit analyzes the video received by the reception unit and identifies specific scenes and events. The analysis unit, for example, converts audio data in the video into text using speech recognition technology and identifies important keywords and phrases. The analysis unit can also analyze video data in the video using image recognition technology and detect specific scenes and events. The analysis unit can also analyze text extracted from audio data using natural language processing to understand the content of the video. The analysis unit uses machine learning to identify interesting or important parts using a model learned from past data. For example, the analysis unit converts audio data in the video into text using speech recognition technology and identifies important keywords and phrases. The analysis unit can also analyze video data in the video using image recognition technology and detect specific scenes and events. Furthermore, the analysis unit can analyze text extracted from audio data using natural language processing to understand the content of the video. The analysis unit uses machine learning to identify interesting or important parts using a model learned from past data. The editing unit edits the parts identified by the analysis unit to enable the user to watch the video efficiently. For example, the editing unit edits the video so that only specific scenes or events are played continuously. The editing unit can also allow the user to skip the extracted parts or adjust the playback speed. This allows the video viewing support system according to the embodiment to efficiently watch only the interesting or important parts of the video that the user wants to watch. Some or all of the above-mentioned processing in the editing unit may be performed using, for example, AI, or may be performed without using AI. For example, the editing unit can perform editing using an AI model that inputs the parts identified by the analysis unit and outputs editing results.

[0065] The reception unit can select a video by inputting a video URL or uploading a video file. Examples of video URLs include, but are not limited to, protocols such as HTTP, HTTPS, and FTP. Examples of video files include, but are not limited to, formats such as MP4, AVI, and MKV. The reception unit can select a video by, for example, a user inputting a video URL. The reception unit can also select a video by, for example, a user uploading a video file. This allows a user to select a video by inputting a video URL or uploading a video file. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the URL of a video input by the user to a generation AI and cause the generation AI to select a video.

[0066] The analysis unit can convert audio data in a video into text using speech recognition technology and identify important keywords and phrases. Examples of speech recognition technology include, but are not limited to, speech recognition algorithms and speech data preprocessing methods. Examples of important keywords and phrases include, but are not limited to, criteria such as frequency and context dependency. The analysis unit can convert audio data in a video into text using speech recognition technology. The analysis unit can also identify important keywords and phrases from the text data. Thus, important keywords and phrases in a video can be identified using speech recognition technology. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input audio data into a generation AI and have the generation AI convert the audio data into text data and identify important keywords and phrases.

[0067] The analysis unit can analyze video data in a video using image recognition technology to detect specific scenes or events. Image recognition technology includes, but is not limited to, object detection algorithms and image data preprocessing methods. Specific scenes or events include, but are not limited to, criteria such as action scenes, moving scenes, and important events. The analysis unit can also analyze video data in a video using image recognition technology. The analysis unit can also detect specific scenes or events. Thus, specific scenes or events in a video can be detected using image recognition technology. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input video data to a generation AI and cause the generation AI to detect specific scenes or events.

[0068] The analysis unit can analyze text extracted from audio data using natural language processing to understand the content of the video. Natural language processing includes, but is not limited to, techniques such as morphological analysis, grammatical analysis, and semantic analysis. Understanding the content of the video can include, but is not limited to, criteria such as summarizing the text and extracting important information. The analysis unit can analyze the text extracted from audio data using, for example, natural language processing. The analysis unit can also understand the content of the video based on the text data. This allows the content of the video to be understood using natural language processing. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input text data to a generation AI and have the generation AI understand the content of the video.

[0069] The analysis unit can identify interesting and important parts using a model learned from past data through machine learning. Machine learning includes, but is not limited to, techniques such as supervised learning, unsupervised learning, and reinforcement learning. Interesting and important parts can include, but are not limited to, criteria such as viewer reactions and number of views. The analysis unit can identify interesting and important parts using, for example, a model learned from past data through machine learning. In this way, interesting and important parts can be identified using machine learning. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input past data into a generation AI and cause the generation AI to identify interesting and important parts.

[0070] The editing unit can edit only specific scenes or events to be played continuously. Specific scenes or events include, but are not limited to, action scenes, moving scenes, and important events. The editing unit, for example, edits only specific scenes or events to be played continuously. This allows users to watch efficiently by editing only interesting or important parts to be played continuously. Some or all of the above-described processing in the editing unit may be performed using, for example, AI, or may be performed without using AI. For example, the editing unit can input specific scenes or events into a generation AI and have the generation AI edit the scenes or events for continuous playback.

[0071] The editing unit allows the user to skip the extracted portion or adjust the playback speed. The extracted portion may be determined based on criteria such as, but not limited to, importance and viewer interest. The playback speed may be determined based on, but not limited to, 1.5x speed or 2x speed. The editing unit may allow the user to skip the extracted portion. The editing unit may also allow the user to adjust the playback speed. This allows the user to skip the extracted portion or adjust the playback speed. Some or all of the above-described processing in the editing unit may be performed using, or without, AI. For example, the editing unit may input the extracted portion into a generation AI and have the generation AI skip or adjust the playback speed.

[0072] The reception unit can estimate the user's emotions and adjust the video selection method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize the video selection procedure. Furthermore, if the user is relaxed, the reception unit can provide detailed selection options and suggest a customizable selection method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable quick video selection. This allows for more appropriate video selection by adjusting the video selection method based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the video selection method.

[0073] The reception unit can analyze the user's past viewing history and suggest an optimal video selection method. For example, the reception unit can automatically display related videos as candidates based on the genre of videos the user has previously viewed. The reception unit can also prioritize suggesting selection methods (audio, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest videos that the user will watch during a specific time period based on the user's past viewing history. In this way, the optimal video selection method can be suggested by analyzing the user's past viewing history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input past viewing history data into a generation AI and cause the generation AI to suggest an optimal video selection method.

[0074] The reception unit can filter videos based on the user's current areas of interest when receiving the videos. For example, the reception unit can preferentially display videos related to topics in which the user is currently interested. The reception unit can also filter related videos based on keywords recently searched by the user. Furthermore, the reception unit can analyze the user's social media activity and suggest videos based on the user's current areas of interest. This makes it possible to provide highly relevant videos by filtering based on the user's current areas of interest. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's interest data into a generation AI and have the generation AI perform the filtering.

[0075] When accepting a video, the acceptance unit can select the optimal acceptance means according to the user's input method. For example, when a user inputs a video URL by voice, the acceptance unit automatically recognizes the URL using voice recognition technology. Furthermore, when a user inputs a video title by text, the acceptance unit can search for and display related videos. Furthermore, when a user uploads an image, the acceptance unit can search for related videos using image recognition technology. This allows for the efficient acceptance of videos by selecting the optimal acceptance means according to the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's input data into a generation AI and have the generation AI select the optimal acceptance means.

[0076] The reception unit can estimate the user's emotions and determine the priority of the videos to be received based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize displaying videos with high entertainment value. Furthermore, if the user is relaxed, the reception unit can prioritize displaying videos with relaxing content. Furthermore, if the user is stressed, the reception unit can prioritize displaying videos that are useful for relieving stress. This allows for more appropriate videos to be provided by determining the priority of videos based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the videos.

[0077] When accepting videos, the reception unit can prioritize accepting highly relevant videos by taking into account the user's geographical location information. For example, the reception unit can prioritize displaying videos of tourist spots related to the user's current location. Furthermore, if the user is in a specific area, the reception unit can also prioritize displaying videos related to news and events in that area. Furthermore, if the user is traveling, the reception unit can also prioritize displaying videos related to the user's travel destination. In this way, highly relevant videos can be provided by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data into the generation AI and cause the generation AI to prioritize accepting highly relevant videos.

[0078] When receiving a video, the reception unit can analyze the user's social media activity and suggest related videos. The reception unit can, for example, suggest content related to videos shared by the user on social media. The reception unit can also analyze the content posted by the user on social media and suggest related videos. Furthermore, the reception unit can also suggest related videos by referring to the activity of the user's friends on social media. In this way, related videos can be provided by analyzing the user's social media activity. Some or all of the above-mentioned processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and cause the generation AI to suggest related videos.

[0079] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a video. For example, the reception unit can preferentially display genres of videos that the user has previously rated highly. The reception unit can also exclude genres of videos that the user has previously rated poorly. Furthermore, the reception unit can suggest an optimal reception method based on the user's past feedback. This makes it possible to provide an optimal reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's feedback data into the generation AI and cause the generation AI to customize the reception method.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can prioritize relaxing scenes in the analysis. Furthermore, if the user is excited, the analysis unit can prioritize entertaining scenes in the analysis. Furthermore, if the user is stressed, the analysis unit can prioritize stress-relieving scenes in the analysis. This allows for more appropriate analysis by adjusting the analysis criteria based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis criteria.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the video. For example, the analysis unit performs a detailed analysis for a video with many important scenes. The analysis unit can also perform a simplified analysis for a video with few important scenes. Furthermore, the analysis unit can identify important scenes according to the genre of the video and adjust the level of detail of the analysis. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the video. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the video. For example, in the case of a movie, the analysis unit applies an algorithm that identifies important scenes based on the development of the story. In addition, in the case of a lecture video, the analysis unit can apply an algorithm that identifies important scenes based on the points emphasized by the lecturer. Furthermore, in the case of a news video, the analysis unit can apply an algorithm that identifies important scenes based on the importance of the news. In this way, by applying different analysis algorithms depending on the category of the video, more accurate analysis can be performed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video category data into the generation AI and cause the generation AI to apply different analysis algorithms.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis, for example, based on the analysis results of videos the user has watched in the past. The analysis unit can also adjust the analysis algorithm by referring to the user's past feedback. Furthermore, the analysis unit can analyze the user's past viewing history and improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotions and determine analysis priorities based on the estimated user emotions. For example, if the user is excited, the analysis unit can prioritize analyzing scenes that are highly entertaining. Furthermore, if the user is relaxed, the analysis unit can prioritize analyzing scenes that are relaxing. Furthermore, if the user is stressed, the analysis unit can prioritize analyzing scenes that are useful for relieving stress. This allows for more appropriate analysis by determining analysis priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priorities.

[0085] During analysis, the analysis unit can determine the analysis priority based on the time of submission of the video. For example, the analysis unit prioritizes analysis of the most recent video. The analysis unit can also determine the analysis priority based on a deadline specified by the user. Furthermore, the analysis unit can also determine the analysis priority taking into account the publication date of the video. This allows for efficient analysis by determining the analysis priority based on the time of submission of the video. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video submission time data into the generation AI and have the generation AI determine the analysis priority.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the videos. For example, the analysis unit prioritizes analysis of videos related to a video the user wants to watch. The analysis unit can also adjust the order of analysis taking into account the relevance of videos the user has previously watched. Furthermore, the analysis unit can prioritize analysis of highly relevant videos based on the genre or theme of the video. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the videos. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input video relevance data into a generation AI and have the generation AI adjust the order of analysis.

[0087] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user is a beginner, the analysis unit can also provide analysis results that avoid technical terminology. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's technical expertise data into a generation AI and have the generation AI adjust the use of technical terminology.

[0088] The editing department can estimate the user's emotions and adjust the editing expression style based on the estimated user's emotions. For example, if the user is relaxed, the editing department can apply a relaxing editing style. Furthermore, if the user is excited, the editing department can apply an entertaining editing style. Furthermore, if the user is stressed, the editing department can apply an editing style that helps relieve stress. This allows for more appropriate editing by adjusting the editing expression style based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the editing department can be performed using, for example, AI, or without AI. For example, the editing department can input the user's emotion data into the generation AI and have the generation AI adjust the editing expression style.

[0089] During editing, the editing department can adjust the level of editing detail based on the importance of the video. For example, the editing department performs detailed editing for a video with many important scenes. The editing department can also perform simplified editing for a video with few important scenes. Furthermore, the editing department can identify important scenes according to the genre of the video and adjust the level of editing detail. This allows for efficient editing by adjusting the level of editing detail based on the importance of the video. Some or all of the above-described processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input video importance data into the generation AI and have the generation AI adjust the level of editing detail.

[0090] During editing, the editing department can apply different editing algorithms depending on the category of the video. For example, in the case of a movie, the editing department applies an algorithm that identifies important scenes based on the development of the story. In addition, in the case of a lecture video, the editing department can apply an algorithm that identifies important scenes based on the points emphasized by the lecturer. Furthermore, in the case of a news video, the editing department can apply an algorithm that identifies important scenes based on the importance of the news. In this way, by applying different editing algorithms depending on the category of the video, more accurate editing can be performed. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input video category data into the generation AI and have the generation AI apply different editing algorithms.

[0091] During editing, the editing department can improve the accuracy of editing by referring to the user's past editing results. The editing department can improve the accuracy of editing, for example, based on the editing results of videos that the user has viewed in the past. The editing department can also adjust the editing algorithm by referring to the user's past feedback. Furthermore, the editing department can analyze the user's past viewing history and improve the accuracy of editing. In this way, the accuracy of editing is improved by referring to the user's past editing results. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input past editing result data into the generation AI and have the generation AI improve the accuracy of editing.

[0092] The editing unit can estimate the user's emotions and adjust the length of the edited content based on the estimated user emotions. For example, if the user is in a hurry, the editing unit can make the edit short and to the point. If the user is relaxed, the editing unit can make the edit longer, including detailed explanations. Furthermore, if the user is excited, the editing unit can add visually stimulating effects. This allows for more appropriate editing by adjusting the length of the edited content based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the editing unit can be performed using, for example, an AI, or without an AI. For example, the editing unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the edited content.

[0093] During editing, the editing department can determine the priority of editing based on the time of submission of the video. For example, the editing department prioritizes editing the most recent video. The editing department can also determine the priority of editing based on a deadline specified by the user. Furthermore, the editing department can determine the priority of editing taking into account the release date of the video. This allows for efficient editing by determining the priority of editing based on the time of submission of the video. Some or all of the above-described processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input video submission time data into the generation AI and have the generation AI determine the priority of editing.

[0094] During editing, the editing department can adjust the editing order based on the relevance of the videos. For example, the editing department prioritizes editing videos related to a video that the user wants to watch. The editing department can also adjust the editing order taking into account the relevance of videos that the user has previously watched. Furthermore, the editing department can also prioritize editing videos that are highly relevant based on the genre or theme of the video. This allows for efficient editing by adjusting the editing order based on the relevance of the videos. Some or all of the above-described processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input video relevance data into a generation AI and have the generation AI adjust the editing order.

[0095] During editing, the editing department can adjust the use of technical terminology in the editing depending on the user's level of expertise. For example, if the user has technical expertise, the editing department can provide an editing result that uses a lot of technical terminology. Also, if the user is a beginner, the editing department can provide an editing result that avoids technical terminology. Furthermore, the editing department can adjust the level of detail of the editing result depending on the user's level of expertise. This allows for more appropriate editing results to be provided by adjusting the use of technical terminology in the editing depending on the user's level of expertise. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input the user's technical expertise data into a generation AI and have the generation AI adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, analysis unit, and editing unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives input of a video that the user wants to watch. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and converts audio data of the video into text and identifies important keywords and phrases. The editing unit is realized, for example, by the control unit 46A of the smart device 14 and edits the identified portion to enable the user to watch efficiently. Furthermore, the reception unit can estimate the user's emotions and adjust the video selection method based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, and editing unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives input of a video the user wants to watch. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and converts audio data of the video into text and identifies important keywords and phrases. The editing unit is realized, for example, by the control unit 46A of the smart glasses 214 and edits the identified portions to enable the user to watch efficiently. Furthermore, the reception unit can estimate the user's emotions and adjust the video selection method based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and editing unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives input of a video that the user wants to watch. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and converts audio data of the video into text and identifies important keywords and phrases. The editing unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and edits the identified portion to enable the user to watch efficiently. Furthermore, the reception unit can estimate the user's emotions and adjust the video selection method based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and editing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives input of videos that the user wants to watch. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and converts audio data of the video into text and identifies important keywords and phrases. The editing unit is realized, for example, by the control unit 46A of the robot 414 and edits the identified parts to enable the user to watch efficiently. Furthermore, the reception unit can estimate the user's emotions and adjust the video selection method based on the estimated emotions.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The analysis unit can identify scenes and events that a user likes based on the user's viewing history. For example, it can analyze scenes that the user watched for a particularly long time or scenes that have been played many times among videos that the user has watched in the past. It can also identify scenes that the user has given high ratings to or left comments about. It can also analyze scenes that the user stopped watching or skipped, and identify scenes that the user does not like. In this way, the analysis unit can identify scenes and events that the user likes based on the user's viewing history, and provide a more personalized video viewing experience.

[0098] The editorial department can estimate the user's emotions and adjust the playback order of videos based on the estimated user emotions. For example, if the user is relaxed, a relaxing scene can be played first. If the user is excited, a highly entertaining scene can be played first. Furthermore, if the user is stressed, a scene that helps relieve stress can be played first. In this way, by adjusting the playback order of videos based on the user's emotions, a more appropriate video viewing experience can be provided.

[0099] The reception unit can adjust the video selection method based on the user's current activity status. For example, if the user is exercising, voice input can be prioritized to allow hands-free video selection. Alternatively, if the user is working, text input can be prioritized to allow quiet video selection. Furthermore, if the user is relaxing, detailed selection options can be provided and a customizable selection method can be suggested. This allows for more appropriate video selection by adjusting the video selection method based on the user's current activity status.

[0100] The analysis unit can estimate the user's emotions based on the content of the video. For example, it can estimate that the user is moved by a moving scene and excited by an action scene. It can also estimate that the user is laughing by a comedy scene and sad by a sad scene. Furthermore, it can estimate changes in the user's emotions in real time based on changes in the audio and visuals of the video. This allows the analysis unit to estimate the user's emotions based on the content of the video and provide a more personalized video viewing experience.

[0101] The editorial department can estimate the user's emotions and adjust the video effects based on the estimated user emotions. For example, if the user is relaxed, a calming effect can be applied. If the user is excited, a visually stimulating effect can be applied. Furthermore, if the user is stressed, an effect that helps relieve stress can be applied. In this way, by adjusting the video effects based on the user's emotions, a more appropriate video viewing experience can be provided.

[0102] The analysis unit can apply different analysis criteria depending on the genre of the video. For example, in the case of a movie, criteria for identifying important scenes based on the development of the story can be applied. In addition, in the case of a lecture video, criteria for identifying important scenes can be applied based on the points emphasized by the lecturer. Furthermore, in the case of a news video, criteria for identifying important scenes can be applied based on the importance of the news. In this way, by applying different analysis criteria depending on the genre of the video, more accurate analysis can be performed.

[0103] The editing department can apply an editing style that a user prefers based on the user's viewing history. For example, the editing department can analyze the editing style that the user has watched for a particularly long time among the videos that the user has watched in the past. It can also identify editing styles that the user has given high ratings to or left comments about. It can also analyze editing styles that the user has stopped watching or skipped, and identify editing styles that the user does not prefer. This allows the editing department to apply an editing style that the user prefers based on the user's viewing history and provide a more personalized video viewing experience.

[0104] The reception unit can estimate the user's emotions and adjust the video recommendation method based on the estimated user's emotions. For example, if the user is relaxed, a relaxing video can be recommended. If the user is excited, a highly entertaining video can be recommended. Furthermore, if the user is stressed, a video that helps relieve stress can be recommended. In this way, by adjusting the video recommendation method based on the user's emotions, more appropriate videos can be recommended.

[0105] The analysis unit can collect video viewing data in real time and dynamically update the analysis results. For example, if a user pauses a particular scene while watching, the importance of that scene can be reevaluated. It can also analyze scenes that the user skips while watching and lower their importance. It can also identify scenes where the user left comments while watching and increase their importance. This allows the analysis unit to perform more accurate analysis by collecting video viewing data in real time and dynamically updating the analysis results.

[0106] The editorial department can estimate the user's emotions and adjust the audio of the video based on the estimated user's emotions. For example, if the user is relaxed, a calm audio can be applied. If the user is excited, an energetic audio can be applied. Furthermore, if the user is stressed, a relaxing audio can be applied. In this way, by adjusting the audio of the video based on the user's emotions, a more appropriate video viewing experience can be provided.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The reception unit accepts input of the video the user wants to watch. The user selects a video by entering the video URL or uploading a video file. The video can be a movie, a TV show, an online video, or anything else. Step 2: The analysis unit analyzes the video received by the reception unit and identifies specific scenes and events. The analysis unit uses speech recognition technology to convert the audio data in the video into text and identify important keywords and phrases. It can also use image recognition technology to analyze the video data and detect specific scenes and events. It then uses natural language processing to analyze the text extracted from the audio data and understand the content of the video. The analysis unit uses machine learning to identify interesting and important parts using a model learned from past data. Step 3: The editing department edits the parts identified by the analysis department to allow users to watch efficiently. The editing department edits the video so that only specific scenes or events are played continuously. Users can also skip the extracted parts or adjust the playback speed. The editing process may or may not be performed using AI.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 reception unit that receives input of a video that a user wants to watch; an analysis unit that analyzes the video received by the reception unit and identifies specific scenes or events; an editing unit that edits the portion identified by the analysis unit so that the user can view it efficiently. A system characterized by:

2. The reception unit Select a video by entering a video URL or uploading a video file 2. The system of claim 1.

3. The analysis unit Uses speech recognition technology to convert audio data in videos into text and identify important keywords and phrases 2. The system of claim 1.

4. The analysis unit Image recognition technology is used to analyze video data and detect specific scenes and events.

2. The system of claim 1.

5. The analysis unit Uses natural language processing to analyze text extracted from audio data and understand the content of the video 2. The system of claim 1.

6. The analysis unit Using machine learning, models learned from past data are used to identify interesting and important parts.

2. The system of claim 1.

7. The editorial department Editing specific scenes or events to play continuously 2. The system of claim 1.

8. The editorial department Allows users to skip extracted parts and adjust playback speed 2. The system of claim 1.

9. The reception unit Estimate user emotions and adjust video selection methods based on the estimated user emotions 2. The system of claim 1.

10. The reception unit Analyze users' past viewing history and suggest optimal video selection methods 2. The system of claim 1.

Images