Contextual Audience Profiles for Privacy-Safe Digital Content
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Solution Overview
Problem
Existing digital component provisioning systems face challenges in efficiently identifying and serving relevant digital content to client devices without relying on personal data or browsing history, leading to resource-intensive processing and privacy concerns.
Innovation Solution
The system employs contextual feature-driven audience interest profiles, generated using a trained contextual model, to identify and provide relevant digital components to client devices during browsing sessions, without collecting personally identifiable data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional digital component provisioning systems process and analyze browsing history and client device data to identify digital components, then the relevance and personalization of provided content is improved, but processing complexity and computational resource requirements increase significantly
Solution Approach 1:
The patent segments the content identification process into two distinct phases: (1) a pre-processing phase where contextual features are extracted and audience interest profiles are generated from browsing sessions, and (2) a serving phase where pre-identified digital components are provided based on matching audience profiles. This segmentation reduces real-time processing complexity while maintaining content relevance accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-processing browsing sessions to extract contextual features and pre-generating audience interest profiles before actual content serving occurs. This allows the system to prepare identification criteria in advance, reducing computational burden during live content provision while maintaining high relevance accuracy.
2Adaptability or versatility
If conventional systems collect and process personally identifiable data and browsing history to serve digital components, then content personalization is improved, but privacy protection and data security deteriorate
Solution Approach 1:
The patent extracts and removes personally identifiable information from browsing data during the pre-processing phase. The system processes only anonymized contextual features (such as browsing patterns, device type, and content categories) while deliberately excluding personal identifiers, thus achieving content personalization without compromising user privacy or data security.
3Measurement precision
If content servers process millions of available digital components in real-time to identify relevant content, then content selection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary matching between audience interest profiles and digital components during the pre-processing phase, before actual content serving. By pre-identifying which digital components match which audience profiles, the system eliminates the need for real-time searching through millions of components, thus maintaining high selection accuracy while dramatically reducing processing time during live operations.
4Measurement precision
If comprehensive contextual features are collected during browsing sessions to generate accurate audience profiles, then profile accuracy is improved, but data processing load and storage requirements increase
Solution Approach 1:
The patent extracts only the essential contextual features from browsing sessions that are necessary for generating accurate audience profiles, such as browsing patterns, content categories visited, and device characteristics. By selectively extracting only relevant features and discarding redundant data, the system maintains high profile accuracy while minimizing data volume and processing load.
Data Source
AI summary
Methods, systems, and media comprising; obtaining, from a client device and during a browsing session conducted by a user, contextual features relating to context within the browsing session, wherein the contextual features do not include any personally-identifiable data; generating, using a trained contextual model and based on the contextual features, an audience interest profile, wherein the audience interest profile represents a prediction of affinity to one or more content categories, wherein the trained contextual model is trained using a set of historical contextual data aggregated from a plurality of prior browsing sessions and audience interest profiles that each represent an affinity to one or more content categories, and wherein the set of historical contextual data does not include any personally-identifiable data; identifying, based on the generated audience interest profile, a digital component for provision; and providing, for display on the client device and during the browsing session, the digital component.