Interactive Ad Format Selection via Presentation Layers
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Solution Overview
Problem
Current digital advertising methods lack the ability to dynamically adapt and optimize interactive advertisements in real-time based on user interactions and ad slot characteristics, leading to suboptimal engagement and outcomes.
Innovation Solution
A method that transforms static digital advertisements into interactive ones by combining extracted assets with presentation layers, using machine learning to predict and select the most engaging format and layout based on real-time ad slot characteristics, user behavior, and defined target outcomes, thereby optimizing user interaction and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static digital advertisements are used, then deployment is simple and fast, but user engagement and interaction are limited
Solution Approach 1:
The patent transforms static advertisements into dynamic interactive advertisements that can adapt their presentation layers based on real-time user interactions and ad slot characteristics. The system dynamically selects and switches between different presentation layers (video, carousel, split-screen, etc.) to maximize engagement while maintaining rapid deployment through automated generation.
Solution Approach 2:
The system changes the parameters of advertisements by transforming static assets into multiple interactive formats with different engagement characteristics. Machine learning models predict optimal presentation layer parameters based on ad slot features and user behavior, allowing the same ad assets to perform differently across various contexts without manual reconfiguration.
2Adaptability or versatility
If interactive advertisement formats are implemented, then user engagement improves, but complexity of ad management increases
Solution Approach 1:
The system implements self-service through automated machine learning models that independently analyze ad slot characteristics, predict optimal presentation layers, and generate interactive advertisements without manual intervention. The closed-loop system continuously learns from user interactions and automatically optimizes ad performance, eliminating the need for complex manual management while maintaining high engagement capabilities.
Solution Approach 2:
The patent incorporates feedback mechanisms where user interactions with interactive advertisements are continuously monitored and fed back to the machine learning models. This feedback loop enables the system to learn from real-world performance data and automatically refine its prediction algorithms, reducing management complexity while improving engagement outcomes over time.
3Adaptability or versatility
If multiple ad formats and presentation layers are tested, then optimal engagement is achieved, but time and resources are consumed
Solution Approach 1:
The system performs preliminary action by pre-generating multiple presentation layers for each advertisement asset before deployment. Machine learning models predict the optimal presentation layer based on ad slot characteristics and historical performance data, allowing the system to serve the best format immediately without requiring extensive A/B testing. This preliminary preparation reduces testing time while maintaining optimization capabilities.
4Adaptability or versatility
If real-time adaptation based on user interactions is implemented, then engagement metrics improve, but computational requirements increase
Solution Approach 1:
The system performs preliminary computation by pre-training machine learning models offline using historical data, so that real-time adaptation requires only lightweight prediction operations. The models are prepared in advance to quickly evaluate ad slot characteristics and predict optimal presentation layers, reducing real-time computational energy consumption while maintaining real-time adaptation capabilities.
Data Source
AI summary
One variation of a method includes: receiving a request for advertising content from an ad slot loaded with a webpage rendered by a computing device; accessing a set of ad slot characteristics including an address of the webpage; extracting a set of visual assets, representing advertising content, from a digital advertisement selected for presentation within the ad slot; selecting a presentation layer, in a set of presentation layers, for pairing with the digital advertisement based on the set of visual assets and the set of ad slot characteristics, the presentation layer defining an interactive format and a layout of visual assets within the interactive format; transforming the digital advertisement into an interactive advertisement according to the presentation layer; and rendering the interactive advertisement within the ad slot responsive to an event that moves the ad slot within a viewing window of the computing device.