Set-Top Box Ad Personalization via User Behavior Tracking
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Solution Overview
Problem
Existing methods for tailoring television advertisements are too coarse, failing to account for individual user preferences and behavior, resulting in suboptimal ad relevance.
Innovation Solution
A system that tracks user behavior through metadata tags, using Bayesian filtering to score and prioritize content items based on non-skipped viewings, inferring user interest and delivering personalized content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If coarse-level tailoring of advertisements is used (regional or time-based), then device complexity is reduced and ease of operation is improved, but ad relevance to individual users deteriorates
Solution Approach 1:
The patent segments the audience from coarse (regional/time-based) to fine (individual household/user) levels by introducing set-top box level segmentation. Each set-top box maintains separate user profiles and tracks individual viewing behaviors, enabling precise targeting while keeping the overall system manageable through distributed processing.
Solution Approach 2:
The system implements feedback loops where user viewing behaviors (watching, skipping, recording) are continuously tracked and fed back into the scoring system. Metadata tags associated with viewed content update user interest scores, which in turn influence future ad delivery decisions, creating a self-improving relevance system.
2Measurement precision
If individual user behavior tracking is implemented, then ad relevance is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent introduces metadata tags as intermediaries between content and user behavior tracking. Instead of directly analyzing complex video content, the system uses pre-defined metadata tags (e.g., product categories, themes) to represent content characteristics, simplifying the tracking and scoring process while maintaining measurement precision.
Solution Approach 2:
The set-top box automatically tracks user behaviors and updates scoring without requiring external intervention. The system self-manages data collection, processing, and application by automatically monitoring viewing patterns and adjusting ad delivery based on computed interest scores.
3Adaptability or versatility
If user behavior data is collected and stored, then personalized content delivery is improved, but loss of user privacy increases
Solution Approach 1:
The patent implements local quality by processing and storing user behavior data at the set-top box level rather than centralizing it. Each set-top box maintains local user profiles and behavior data, enabling personalization while keeping sensitive information localized and reducing centralized privacy risks.
Data Source
AI summary
A user is given the ability to control the display of content items such as advertisements, by for example skipping over content items that are not of interest. Metadata tags associated with non-skipped viewings of content are stored and tracked. Subsequently, candidate content items are scored according to their metadata tags, giving higher scores to candidate content items associated with higher occurrences of non-skipped viewings. The higher-scoring candidate content items can then be favored over other candidate content items. Thus, based on the choices the user makes with respect to skipping or not skipping particular content items, inferences are made as to the user's level of interest in various subjects, and subsequent content items are delivered in a personalized manner to the user.