Synthesized Audiences for Privacy-Constrained Content Targeting
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Solution Overview
Problem
Large content presentation services face challenges in selecting content items that are well-matched to consumers due to varying availability of consumer identity information, opt-in preferences, and regulatory factors, making it difficult to target content effectively.
Innovation Solution
Utilizing probabilistic models based on contextual information to create synthesized audiences without relying on consumer identity, allowing content items to be selected and presented to unidentified consumers by inferring their interests from behavior-dependent audiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content presentation services rely on consumer identity information for content selection, then content matching precision is improved, but the system becomes vulnerable to privacy constraints, opt-out preferences, and regulatory limitations
Solution Approach 1:
The patent introduces synthesized audiences as an intermediary mechanism between content presentation services and consumers. Instead of directly using consumer identity information, the system creates synthesized audience representations based on contextual information (device type, location, time, content interactions) that serve as mediators to infer consumer preferences and characteristics, thereby enabling content matching without direct access to or reliance on consumer identity data
Solution Approach 2:
The patent replaces the traditional identity-based content selection mechanism with a context-based probabilistic model. Instead of using deterministic identity matching, the system employs statistical models that analyze contextual patterns to probabilistically determine consumer preferences, substituting the mechanical identity verification process with a statistical inference approach that is more adaptable to privacy constraints
2Reliability
If detailed consumer identification is used for content targeting, then content relevance is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent extracts and processes only the necessary contextual information elements (device type, location, time, content interactions) while discarding or ignoring detailed consumer identification data. By selectively extracting and processing only the essential contextual features needed for audience synthesis, the system achieves content relevance without the complexity of processing and managing comprehensive consumer identification systems
Solution Approach 2:
The patent changes the fundamental parameters used for content selection from consumer identity parameters to contextual parameters. Instead of using static identity attributes, the system dynamically analyzes contextual parameters such as device characteristics, geographic location, temporal information, and content interaction patterns, transforming the selection process into a dynamic probabilistic assessment that is less complex and more adaptable
3Adaptability or versatility
If probabilistic models based on contextual information are used to create synthesized audiences, then the system can target content to unidentified consumers, but the precision of consumer targeting may be reduced compared to identity-based approaches
Solution Approach 1:
The patent performs preliminary analysis of contextual information to create synthesized audience representations before content selection occurs. By pre-processing and synthesizing audience profiles based on contextual patterns, the system prepares probabilistic consumer models in advance, enabling rapid content matching to unidentified consumers without requiring real-time identity verification, thus balancing precision with adaptability
Data Source
AI summary
A behavior-dependent audience populated with content consumers based on analysis of records of their actions is identified. Rules for including content consumers, without using their identity information, within a synthesized audience are generated based on a relationship determined between membership in the behavior-dependent audience and a set of properties of content presentation triggering events. In response to a particular content presentation triggering event, a content item which is targeted to the behavior-dependent audience is presented to a particular content consumer who satisfies a rule for inclusion in the synthesized audience.