Site-Specific Ad Tags for Dynamic Creative Allocation

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Solution Overview

Problem

Existing digital advertising campaigns struggle to dynamically adjust ad creatives based on user behavior and publisher-specific requirements, leading to suboptimal brand lift and inefficient resource allocation across different digital properties.

Innovation Solution

Implementing a site-specific Ad Tag that retrieves publisher-specific configurations and external data to tailor ad creative requests, monitor ad placements, and adjust allocations based on performance data and user feedback, using machine learning algorithms for optimization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If ad creatives are allocated uniformly across digital properties, then implementation is simple, but brand lift is suboptimal due to inability to target high-performing properties

Engineering Contradiction:

Improvebrand liftVSAvoidcampaign management complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system implements continuous feedback loops by monitoring performance data from digital properties and using this information to dynamically adjust ad creative allocations. Performance data is collected, analyzed, and fed back into the allocation algorithm to optimize brand lift while managing complexity through automated decision-making.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The ad creative allocation system transitions from static uniform distribution to dynamic allocation that adapts in real-time based on performance data. The system continuously adjusts allocation weights for different digital properties based on their measured performance, enabling optimized brand lift without manual intervention.

Inventive Principle:

Principle #15Dynamics

2Productivity

If manual monitoring and adjustment of ad creatives is performed, then allocation can be optimized, but time consumption and operational overhead increase significantly

Engineering Contradiction:

Improvecampaign optimization efficiencyVSAvoidtime for monitoring and adjustment

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system enables self-service optimization by automatically monitoring performance data, analyzing results, and adjusting ad creative allocations without human intervention. The automated system serves itself by continuously optimizing campaign performance, eliminating the need for manual monitoring and significantly reducing operational overhead.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processes of monitoring and adjusting ad creatives with an automated computational system. Machine learning algorithms and automated data processing substitute for human analysts, eliminating time-consuming manual operations while maintaining or improving optimization quality.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If performance data is collected and analyzed in real-time, then ad creative allocation can be optimized dynamically, but computational resources and processing time increase

Engineering Contradiction:

Improveallocation accuracyVSAvoidcomputational resource consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by focusing computational resources on the most critical performance metrics and key digital properties rather than processing all available data uniformly. This selective approach maintains high allocation accuracy while reducing unnecessary computational overhead and resource consumption.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250278757A1System and method for digital advertising campaign optimization

Publication Date: 2025.09.04 ACK VENTURES HOLDINGS LLC

AI summary

A technique for dynamically adjusting a digital advertising campaign during an active campaign flight is discussed. Using feedback from a digital survey over an exposed audience of user populations, brand lift may be calculated on a per ad creative and/or per site basis. User characteristics derived from content consumption patterns may be used to optimize ongoing campaigns and formulate target audiences and target creative formats for new campaigns.