Live Ad Placement Using AI Content Metadata and User Preferences
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional advertising placement for live stream applications is inadequate as it does not consider both content metadata and user preferences simultaneously, leading to suboptimal ad targeting.
Innovation Solution
A system that integrates AI-driven content analysis during encoding to extract advertising-relevant metadata, which is then used by a content-aware ad server to deliver user-specific and content-specific advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional advertising placement methods are used based on predetermined demographics and content type, then the advertising system is simple to implement, but the ad relevance and targeting effectiveness deteriorate
Solution Approach 1:
The system performs preliminary extraction of content metadata during the video encoding process, before ad delivery. This allows the ad server to have ready-to-use content context information when making ad placement decisions, improving targeting precision without adding complexity to the real-time ad serving operation
Solution Approach 2:
The patent introduces an intermediary content metadata extraction system that bridges the video content and the ad server. This intermediary layer analyzes video content to extract relevant metadata (objects, scenes, actions) and makes this information available to the ad server, enabling better ad matching without requiring the ad server to directly analyze video content
2Measurement precision
If dynamic advertisements based on user behavior data are provided, then ad personalization is improved, but the system cannot effectively utilize content metadata for ad placement
Solution Approach 1:
The system merges user preference data with content metadata in the ad server decision-making process. Both user behavior data and extracted content metadata are combined to determine ad placement, creating a more comprehensive and relevant advertising experience that leverages both user context and content context
3Manufacturing precision
If content metadata extraction and AI analysis are integrated into the encoding process, then ad placement accuracy is improved, but the encoding process complexity increases
Solution Approach 1:
The content metadata extraction and AI analysis are performed as preliminary actions during the video encoding process. By extracting metadata at this stage rather than later, the system prepares content context information in advance, enabling accurate ad placement without adding complexity to the video playback or ad serving processes
Solution Approach 2:
The encoding component is enhanced to perform multiple functions: traditional video encoding plus content metadata extraction. This multi-functional approach consolidates operations into a single process, improving ad placement accuracy while managing complexity through integration rather than adding separate systems
Data Source
AI summary
Techniques relating to live advertising management are disclosed. A method for providing content- and user-specific advertising placement includes receiving a video content, encoding the video content using a cloud encoding component, thereby generating encoded video content, extracting content metadata from the video content, generating extracted and advertising-relevant content metadata from the content metadata using an AI model, providing the encoded video content with the extracted and advertising-relevant content metadata to a video player, and determining an advertising placement using a content-aware ad server based on the extracted and advertising-relevant content metadata and a set of user preferences. The extracted and advertising-relevant content metadata may be provided using in-band metadata embedding or out-of-band metadata embedding.