Audience Analysis Using Automated Content Recognition
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Solution Overview
Problem
Managing large datasets for media content distribution systems is challenging due to difficulties in storing, indexing, and calculating metrics based on matched data, which hinders efficient audience analysis and content targeting.
Innovation Solution
A targeting system is developed that includes user interfaces such as audience explorer, genre explorer, show explorer, and station explorer to assist administrators in selecting user categories and viewing metrics, utilizing automated content recognition and identity management to correlate media consumption data and viewing behavior, enabling real-time updates and precise content targeting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large datasets are stored and managed for media content distribution systems, then audience analysis capability is improved, but data management complexity and storage requirements increase
Solution Approach 1:
The patent segments large datasets into multiple data partitions distributed across different storage nodes. Each partition contains a subset of the media content data, allowing the system to manage large datasets by dividing them into smaller, more manageable units that can be independently stored, indexed, and queried without overwhelming any single system component.
2Measurement precision
If comprehensive viewing behavior data is collected and stored, then content targeting accuracy is improved, but calculation complexity and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing aggregated viewing metrics and audience segmentation data in advance. When content targeting calculations are needed, the system retrieves these pre-computed results rather than performing complex calculations on raw viewing behavior data in real-time, significantly reducing processing time while maintaining targeting accuracy.
3Productivity
If real-time viewing metrics are calculated from large datasets, then content delivery optimization is improved, but computational load and system resources increase
Solution Approach 1:
The patent applies partial action by calculating viewing metrics for only the relevant data subsets needed for specific content delivery decisions, rather than processing entire large datasets. The system identifies and processes only the portions of data that directly impact the current optimization task, reducing computational load and energy consumption while still achieving effective content delivery optimization.
Data Source
AI summary
The present disclosure relates generally to media content distribution, and more particularly to audience analysis using automated content recognition and identity management. In certain embodiments, a targeting system is provided for generating and presenting a user interface to assist an administrator in targeting particular users. For example, a user interface (referred to as an audience explorer) may be presented that allows the administrator to select one or more categories of users. For another example, a user interface (referred to as a genre explorer) may be presented to that allows the administrator to select a particular media category. For another example, a user interface (referred to as a show explorer) may be presented that allows the administrator to select a particular media segment. For another example, a user interface (referred to as a station explorer) may be presented that allows the administrator to select a particular content stream.