Content Analysis Layer for Brand-Safe Programmatic Ad Placement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing programmatic advertising environments fail to adequately match advertisements with content to ensure brand safety by preventing placement against objectionable content.
Innovation Solution
A computer-implemented advertisement placement system that processes content assets to generate identifiers, categorizes them using global and target brand affinity criteria, and adjusts advertisement presentation based on these criteria to ensure brand safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If programmatic advertising systems automatically place ads without content analysis, then advertising productivity is improved, but brand safety deteriorates due to placement against objectionable content
Solution Approach 1:
The system performs preliminary content analysis by generating content identifiers and comparing them against objectionable content lists before ad placement decisions are made. This preliminary action ensures that ads are not placed against objectionable content while maintaining automated efficiency.
Solution Approach 2:
The patent introduces an intermediary content analysis layer between the ad server and content delivery. This intermediary system generates content identifiers, compares them against curated lists of objectionable content, and makes placement decisions based on the comparison results, thereby ensuring brand safety without manual intervention.
2Reliability
If content analysis is performed for every ad placement, then brand safety is improved, but processing time increases
Solution Approach 1:
Content identifiers are generated and stored in advance in hash tables organized by content categories. When an ad placement request comes in, the system quickly compares the content identifier against pre-curated lists of objectionable content identifiers, rather than analyzing the entire content in real-time.
Solution Approach 2:
Instead of analyzing the actual content data during ad placement decisions, the system uses copied identifiers (hashes or fingerprints) of the content that are stored in advance. These identifiers can be quickly compared against lists of objectionable content identifiers without requiring access to or analysis of the original content.
3Manufacturing precision
If comprehensive content categorization is implemented, then advertising precision is improved through better brand alignment, but system complexity increases
Solution Approach 1:
The content analysis system is segmented into modular components: content identifier generation, hash table organization by category, and comparison against objectionable content lists. This segmentation allows the system to handle complex categorization tasks through simple, independent modules that can be maintained and updated separately.
Solution Approach 2:
The content identifier system serves multiple functions: it uniquely identifies content, categorizes content by type, and enables comparison against various objectionable content lists. This multi-functionality reduces the need for separate systems for each task, thereby managing complexity while achieving comprehensive content analysis.
Data Source
AI summary
Various embodiments comprise systems, methods, architectures, mechanisms, apparatus, or protocols configured to determine whether to present an advertisement contemporaneously with a presentation of user requested content at a user device.