Client-Side Context Classification for Privacy-Safe Streaming Ads
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Streaming media services face challenges in delivering targeted advertisements and personalized content recommendations due to limited visibility into the content being viewed, especially when content is streamed through third-party services, and there are concerns about user privacy and regulatory constraints on data collection.
Innovation Solution
Implementing a machine learning-based classifier on the streaming media client to analyze portions of content, predicting context labels based on video frames and audio information, which are then used by the streaming media service to deliver targeted ads and personalized recommendations without collecting identifying information about the content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a streaming media service collects detailed content viewing data to deliver targeted ads and personalized recommendations, then advertising effectiveness and recommendation accuracy are improved, but user privacy is compromised and regulatory compliance becomes difficult
Solution Approach 1:
The patent introduces a third-party analytics service as an intermediary between the streaming media service and the content viewing data. The analytics service receives encrypted content context information from the streaming media service, performs analysis to generate advertising and recommendation data, and returns results without the streaming media service ever having access to raw viewing data. This intermediary architecture enables precise content context measurement while protecting user privacy by design.
Solution Approach 2:
The patent creates and analyzes copies of content context information rather than using the original detailed viewing data. The streaming media service generates encrypted representations of content being viewed, which are then analyzed by the third-party service to produce advertising and recommendation insights. These copies contain sufficient information for accurate targeting while eliminating direct exposure to sensitive user viewing habits.
2Productivity
If the streaming media service processes content data directly to generate advertising and recommendation insights, then response time and personalization accuracy are improved, but server processing burden and infrastructure costs increase
Solution Approach 1:
The patent extracts the complex data processing and analysis functions from the streaming media service's server infrastructure and relocates them to a specialized third-party analytics service. The streaming media service only needs to send encrypted content context data and receive results, while the computationally intensive tasks of content analysis, advertising selection, and recommendation generation are performed externally by the analytics service's optimized processing systems.
3Productivity
If the streaming media service implements comprehensive content tracking to enable targeted advertising, then advertising revenue potential is improved, but system complexity and data management overhead increase
Solution Approach 1:
The patent makes the third-party analytics service a universal platform that handles multiple functions: content context analysis, advertising selection, recommendation generation, and data management. Instead of the streaming media service implementing separate systems for each function, the unified analytics service performs all these tasks through a single integrated interface, reducing overall system complexity while maintaining comprehensive advertising capabilities.
Data Source
AI summary
Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for performing context classification of streaming content using machine learning (ML). In an embodiment, a streaming media client receives an audio/video (A/V) stream that represents a portion of content to be played back by the client. The client reconstructs a sequence of video frames from the A/V stream, extracts audio information from the A/V stream, and executes an ML based classifier to predict a context label associated with the portion of content based at least on one or more video frames from the sequence of video frames and the audio information. The client then transmits the context label to a streaming media service. The service may use the context label to select an advertisement or content recommendation to send to the client or to select a set of content streaming parameters for the client.