Contextual Content Distribution via Linked Document Analysis
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Solution Overview
Problem
In content distribution, the current context of a candidate content distribution location is often unknown, especially when the content is new or changing, making it difficult to select contextually relevant content, leading to reduced effectiveness in advertising and content delivery.
Innovation Solution
A system that identifies the current context of requested content by analyzing related content, such as search results, and selects alternative content based on this context, using a processor to retrieve and aggregate contextually relevant content from linked documents, keywords, or search queries to ensure relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content distribution is performed without knowing the current context of the candidate location, then the distribution process can proceed quickly, but the relevance and effectiveness of the distributed content deteriorates
Solution Approach 1:
The system performs preliminary context analysis by examining linked documents and their associated advertisements before making content distribution decisions. This preliminary action enables the system to pre-determine contextual relevance without delaying the actual content distribution process, thus maintaining both speed and relevance.
Solution Approach 2:
The patent uses linked documents as intermediary elements to infer the context of the candidate content distribution location. By analyzing the context of these intermediary linked documents and their previously selected advertisements, the system can indirectly determine the appropriate context for content selection without directly analyzing the candidate location's current state.
2Measurement precision
If the system analyzes linked documents to determine current context, then content selection accuracy improves, but the processing time and system complexity increases
Solution Approach 1:
The system copies and reuses context information from linked documents that has already been analyzed and stored. Instead of performing complete re-analysis, the system leverages previously computed context data and associated advertisement selections, significantly reducing processing complexity while maintaining accuracy.
Solution Approach 2:
The system uses feedback from previously selected advertisements and their performance to refine context identification. By analyzing which advertisements were previously deemed relevant to linked documents, the system continuously improves its context understanding without requiring complex new analysis mechanisms.
3Manufacturing precision
If the system uses previously selected advertisements from linked documents as candidates, then contextual relevance improves, but the variety of available content may be limited
Solution Approach 1:
The system dynamically adjusts the content selection process by first identifying highly relevant candidates from previously selected advertisements, then expanding the search to include other potentially relevant content. This dynamic approach ensures both high contextual relevance and sufficient content variety by adapting the selection scope based on initial findings.
Solution Approach 2:
The system applies different selection criteria to different content candidates: previously selected advertisements receive higher priority due to their proven relevance, while other content candidates are evaluated based on their potential relevance. This local quality differentiation ensures that the most relevant content is selected while maintaining flexibility in the overall selection process.
Data Source
AI summary
Contextually relevant pieces of alternative content, including advertisements, are selected for display with requested content based on documents or other second content sources that are referenced by or in the requested content.