Automated Content Clip Curation for Personalized Engagement
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Solution Overview
Problem
Consumers often fail to engage with media content due to lack of familiarity or reluctance to explore, leading to inefficiencies in introducing desirable content to them, as existing methods lack effective automation in content curation.
Innovation Solution
An automated content curation system that utilizes consumption history data and demographic data to identify and isolate the most desirable content clips for users, allowing for personalized promotion and increased engagement through targeted content playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated content curation is implemented, then content promotion effectiveness is improved, but system complexity increases
Solution Approach 1:
The system segments content into discrete clips with specific characteristics (duration, genre, actors, scenes) that can be independently analyzed and curated. This segmentation allows the complex curation task to be broken down into manageable components that can be processed automatically based on consumer preferences.
Solution Approach 2:
The patent introduces an automated curation system that acts as an intermediary between content providers and consumers. This intermediary analyzes consumer preferences and automatically selects appropriate content clips, reducing the complexity burden from end-users while maintaining effective content promotion.
2Ease of operation
If personalized content selection is implemented, then consumer engagement is improved, but data processing requirements increase
Solution Approach 1:
The system applies local quality by tailoring content selection to individual consumer preferences and characteristics. Each consumer receives personalized content clips based on their specific tastes, viewing history, and demographic profile, thereby improving engagement while processing data selectively rather than universally.
Solution Approach 2:
The patent utilizes parameter changes by analyzing various consumer parameters (demographics, viewing history, preferences) and transforming this data into personalized content recommendations. The system changes the parameters of content selection based on individual consumer profiles, enabling personalized engagement without requiring excessive data processing for all users equally.
Data Source
AI summary
A content curation system includes a computing platform having a hardware processor and a system memory storing a content promotion software code providing a user interface. The hardware processor executes the content promotion software code to receive an initiation signal corresponding to a user action, and, in response to receiving the initiation signal, to identify multiple content items as desirable content items to the user. In addition, the content promotion software code determines a portion of the desirable content item as most desirable content to the user, and, for each most desirable content portion, obtains a content clip including that content, resulting in multiple content clips corresponding respectively to the multiple content items. The content promotion software code further outputs the content clips for playout to the user via the user interface.