Media Guidance Application Ad Buffering and Interest Detection
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Solution Overview
Problem
Existing advertising systems struggle to determine if a viewer is actually watching an advertisement and if they are interested in it, making it difficult to deliver relevant and engaging ads.
Innovation Solution
A media guidance application is used to monitor viewer interactions during advertisements, detecting interest through actions such as not changing channels or searching for related content. When interest is detected, the application retrieves metadata about the advertisement to identify relevant characteristics, which are then used to find and display a related or similar advertisement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a second related advertisement is displayed to the viewer after detecting interest in the first advertisement, then the promotional impact for advertisers is increased, but the commercial break time encroaches on the media content restart time
Solution Approach 1:
The system buffers the media content before the commercial break ends, preparing the content in advance so that it can be resumed immediately after the extended advertisement sequence without delay. This preliminary buffering action resolves the time conflict by decoupling the advertisement delivery from the media content timing.
2Device complexity
If advertisements are selected using general viewer demographics information, then the system complexity is reduced, but the relevance and engagement of advertisements with individual viewers decreases
Solution Approach 1:
The system monitors viewer interactions with advertisements (such as watching behavior, searches, or social media activity) and uses this feedback to dynamically adjust advertisement selection. This feedback mechanism enables personalized ad delivery without requiring complex pre-processing of viewer profiles, as the system adapts in real-time based on observed behavior.
3Measurement precision
If the system monitors viewer interactions to detect advertisement interest, then the precision of determining viewer engagement is improved, but the device complexity increases
Solution Approach 1:
The system leverages existing viewer actions and interactions (such as searches, social media posts, or viewing patterns) that viewers naturally perform without being asked. By analyzing these self-generated data points, the system achieves precise engagement detection without requiring additional monitoring hardware or complex intervention mechanisms.
Data Source
AI summary
When a viewer is determined to be interested in an advertisement, a media guidance application may identify a second related advertisement to display to the viewer. The second advertisement may be displayed following the first interesting advertisement. To avoid timing issues, media content following the advertisements may be buffered so that the viewer can catch up on the media content without missing anything.