Adaptive Audio Track Selection via Video Frame Analysis
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Solution Overview
Problem
Traditional video processing approaches require explicit cooperation between content generators and supplemental content applications, leading to inefficiencies when applied across multiple gaming or social media applications, especially when executed in different environments, as they rely on premeditated metadata and API-based configurations.
Innovation Solution
A decoupled approach using machine learned algorithms for deep offline analysis of video data to identify attributes characterizing video content, allowing supplemental content applications to operate independently and generate adaptive audio tracks based on real-time video analysis, leveraging technologies like CNN and HDBSCAN for feature extraction and similarity clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional video processing approaches use explicit metadata and API-based configurations, then content generators can cooperate with supplemental content applications, but the system complexity increases and adaptability decreases when applied across multiple applications in different environments
Solution Approach 1:
The patent extracts the video analysis functionality from the supplemental content application and places it in a separate, reusable video processing module. This module can be independently configured and tested, then deployed across multiple applications without requiring each application to implement its own metadata extraction and analysis logic, thereby reducing overall system complexity while maintaining reliable cooperation.
Solution Approach 2:
The patent creates a universal video processing module that can serve multiple different video applications and supplemental content applications. This module provides standardized interfaces and analysis capabilities that work across different environments, eliminating the need for application-specific configurations and improving adaptability while reducing the burden of maintaining multiple separate implementation approaches.
2Loss of information
If traditional approaches rely on premeditated metadata, then content can be processed with explicit information, but the loss of time for manual metadata creation and configuration increases
Solution Approach 1:
The patent implements automated video analysis that allows the system to extract and analyze video content attributes autonomously without requiring manual metadata creation. The video processing module automatically analyzes video frames, detects objects, actions, and scenes, and generates attribute information on-demand, thereby eliminating the time loss associated with manual metadata preparation while ensuring comprehensive information extraction.
Solution Approach 2:
The patent performs deep offline analysis of video data in advance to pre-compute and store attribute information that can be quickly retrieved during real-time processing. This preliminary analysis phase extracts and organizes video attributes before they are needed for supplemental content generation, reducing the time required during actual content delivery while maintaining comprehensive information availability.
3Adaptability or versatility
If machine learned algorithms perform deep offline analysis of video data, then adaptability and customization improve, but the use of energy and computational resources increases
Solution Approach 1:
The patent performs computationally intensive deep offline analysis of video data in advance, during low-demand periods, to pre-compute attribute vectors and similarity metrics. This preliminary processing extracts comprehensive video attributes and stores them for quick retrieval during real-time supplemental content generation, thereby achieving high adaptability and customization while concentrating energy consumption in advance rather than during active content delivery.
Solution Approach 2:
The patent implements a two-tiered analysis approach where comprehensive deep analysis is performed offline on subsets of video data, and then lighter-weight similarity matching is performed in real-time. This allows the system to achieve high adaptability through thorough offline analysis while keeping real-time computational resource usage and energy consumption at manageable levels by leveraging the pre-computed information.
Data Source
AI summary
Aspects of the present application correspond to generation of supplemental content based on processing information associated with content to be rendered. More specifically, aspects of the present application correspond to the generation of audio track information, such as music tracks, that are created for playback during the presentation of video content. Illustratively, one or more frames of the video content are processed by machine learned algorithm(s) to generate processing results indicative of one or more attributes characterizing individual frames of video content. A selection system can then identify potential music track or other audio data in view of the processing results.