Context Specification for Contextual Search and Content Delivery
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Solution Overview
Problem
Current systems for distributing content, such as advertisements, lack efficiency in targeting contextually relevant websites, often resulting in irrelevant placements due to keyword-based targeting or pre-defined categorization methods, which limits the effectiveness of content delivery.
Innovation Solution
The implementation of machine-learning techniques and algorithms that preprocess large volumes of Internet content to determine optimal placement opportunities by correlating campaign content with context terms, using digital fingerprints and co-occurrence probabilities to identify relevant webpages for real-time bidding and content placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If keyword-based targeting is used to connect content with distributors, then the system can operate with simple matching logic, but the content placement becomes irrelevant due to overly broad keyword matching
Solution Approach 1:
The patent segments the context specification into multiple hierarchical levels: campaign-level context, ad-level context, and exclusion contexts. This segmentation allows the system to move beyond simple keyword matching by evaluating content at different granularities, thereby improving placement relevance while maintaining operational simplicity through structured organization.
Solution Approach 2:
The patent introduces context specifications as an intermediary layer between keywords and content placement decisions. Instead of directly matching keywords to content, the system uses context specifications (including in-context terms, out-of-context terms, and contextual URLs) as mediators to evaluate and determine relevant placements, thus improving precision without sacrificing ease of operation.
2Adaptability or versatility
If pre-defined categorization systems (e.g., IAB categories) are used to specify content context, then the system can operate with standardized classifications, but content generators are limited to selecting from only those predefined categories
Solution Approach 1:
The patent creates a universal context specification framework that can accommodate multiple approaches: predefined categories (such as IAB categories), custom keyword-based contexts, and exclusion-based contexts. This multi-functional system allows content generators to choose from standardized classifications when desired, while also enabling custom context definitions, thereby achieving high adaptability without requiring complete system redesign.
Solution Approach 2:
The context specification system is designed to be dynamic and adaptable. Content generators can start with predefined categories and progressively refine or customize their contexts as needed. The system supports evolving from simple category selection to more sophisticated custom context specifications, allowing flexibility to increase with user needs while maintaining a manageable baseline complexity.
3Measurement precision
If large lists of keywords are specified to define content context, then the system can capture broader context coverage, but the system efficiency decreases due to processing overhead
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing context specifications, contextual URLs, and co-occurrence data before the actual content delivery process. This allows the system to prepare context evaluation frameworks in advance, so that during real-time content delivery, the system can efficiently query pre-computed context relevance rather than processing large keyword lists from scratch, thus maintaining both coverage and efficiency.
Solution Approach 2:
The system transforms the context specification approach by changing parameters from simple keyword lists to structured context specifications including in-context terms, out-of-context terms, contextual URLs, and co-occurrence probabilities. This parameter transformation enables more precise context coverage while improving processing efficiency through structured data organization and pre-computed relevance metrics.
Data Source
AI summary
Systems and systems described herein may generate campaigns and efficiently calculate bids for placement of campaign data into Internet data. Embodiments may calculate context scores for campaign data based on campaign terms and beacon terms. The context scores may be used to identify Internet content that has a high page score. If the page score of particular Internet content exceeds a predetermined threshold, the system may place a bid for a campaign based on disclosed algorithms taking as inputs performance scores, context scores, page scores, campaign budgets, and other parameters. The systems therefore are capable of quickly and effectively calculating optimal bids to place for a particular campaign given parameters disclosed herein.