Contextual Video Editing and Behavioral Clustering
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Solution Overview
Problem
Current video editing and content sharing processes are inaccessible and time-consuming for mobile device users, as they require specialized knowledge and tools, and users face difficulties in finding videos of interest amidst vast content due to the lack of suitable tools for mobile device users and inefficient content discovery methods.
Innovation Solution
A method and system that collect metadata synchronized with video recording, correlate attributes with editors and content, and use behavioral clusters to suggest editors and videos of interest, enabling simplified video editing and content discovery on mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video editing is performed using traditional computer-based systems, then editing quality and functionality are improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent replaces traditional mechanical video editing systems with cloud-based automated editing services. The mobile device captures video and automatically uploads it to remote servers that perform the editing operations, substituting complex local hardware and software with simplified mobile device functions and remote cloud processing.
Solution Approach 2:
The patent introduces an automated editing service as an intermediary between the mobile device and the final edited video. This service receives raw video from the mobile device, performs automated editing based on predefined parameters and user selections, and returns the edited video, thereby eliminating the need for users to directly interact with complex editing software.
2Ease of operation
If video editing is automated using cloud-based services, then ease of operation is improved, but loss of time in uploading and processing increases
Solution Approach 1:
The patent performs preliminary actions by pre-configuring editing parameters, templates, and automation rules before video upload. The system prepares editing workflows in advance, so that when video is uploaded, the automated editing process can immediately apply pre-defined operations, reducing overall processing time.
Solution Approach 2:
The patent enables continuous useful action by allowing users to upload videos and initiate automated editing processes without interruption to their workflow. The system processes videos in the background while users can continue using the mobile device for other tasks, maintaining continuous productivity without waiting for editing completion.
3Ease of operation
If content discovery relies on traditional search methods, then user control is improved, but productivity deteriorates due to time-consuming manual searching
Solution Approach 1:
The patent implements feedback mechanisms by analyzing user interactions with discovered content and using this information to refine and personalize future content recommendations. The system learns from user behavior patterns, preferences, and feedback to continuously improve the accuracy and relevance of suggested videos, making content discovery increasingly efficient over time.
Solution Approach 2:
The patent enables self-service content discovery by automatically analyzing user profiles, viewing history, and preferences to generate personalized video recommendations without requiring manual searching. The system serves users relevant content proactively based on their demonstrated interests and behaviors, eliminating the need for time-consuming manual exploration.
4Productivity
If behavioral clustering is used for content recommendation, then productivity is improved, but loss of information about individual user preferences increases
Solution Approach 1:
The patent applies local quality by creating personalized behavioral clusters for each user based on their specific preferences, behaviors, and interaction patterns. Instead of using a single generic clustering approach, the system tailors the clustering parameters and weightings to reflect individual user characteristics, ensuring that recommendations remain highly relevant while still leveraging the efficiency of cluster-based processing.
Data Source
AI summary
Video editing using contextual data may include collecting metadata from a sensor concurrently with recording a video, wherein the metadata is synchronized in time with the video, detecting attributes within the metadata and attributes from the video, and correlating the attributes with a plurality of editors. An editor may be selected from the plurality of editors according to the correlating and a video editing workflow may be automatically initiated. Content discovery using clusters may include receiving a user request for video content from a device, determining a behavioral cluster for the user according to demographic data for the user, determining a video of interest associated with the behavioral cluster, and providing a video of interest to the device using the processor.