Automated Content Curation via Narrative Structure Analysis
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Solution Overview
Problem
The abundance of online content, particularly short form content segments, makes it challenging to keep viewers engaged for long periods due to the burdensome navigation and lack of cohesiveness between content segments, which often lack a narrative structure.
Innovation Solution
A system utilizing machine learning models to curate content by analyzing segment signature vectors, identifying candidate segments that match narrative portions, and assembling them into playlists that follow a defined narrative structure, thereby enhancing cohesiveness and user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content segments are made short and diverse to provide variety, then user interest is initially captured, but cohesiveness and narrative structure are lost
Solution Approach 1:
The patent segments content into discrete units with specific narrative functions (setup, conflict, resolution) and uses machine learning to identify and assemble segments that follow narrative structures, thereby maintaining coherence while allowing diversity in segment content and source
Solution Approach 2:
The system changes the parameter of narrative structure by training machine learning models to recognize specific narrative patterns and applying these structures to curate diverse content segments, transforming unstructured variety into structured coherence
2Productivity
If automated content curation is implemented to improve efficiency, then playlist assembly speed increases, but narrative coherence may be compromised
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models with labeled narrative data and pre-segmenting content with metadata tags before curation, enabling fast automated assembly that maintains narrative coherence through pre-established structural knowledge
Solution Approach 2:
The system uses feedback mechanisms where the machine learning model evaluates candidate segment combinations against narrative structure criteria, iteratively selecting segments that maximize narrative coherence while maintaining curation efficiency
3Reliability
If manual content selection is used to ensure quality, then narrative structure is maintained, but time consumption and complexity increase
Solution Approach 1:
The patent implements self-service by enabling the machine learning system to autonomously analyze content segments, evaluate narrative coherence, and assemble playlists without manual intervention, maintaining quality through algorithmic narrative structure recognition while eliminating time-consuming manual curation
Solution Approach 2:
The system replaces the mechanical process of manual content selection and analysis with machine learning-based automated analysis, substituting human cognitive effort with computational algorithms that can rapidly evaluate narrative structures across large content volumes
Data Source
AI summary
Systems and methods are described herein for curating content that follows a narrative structure. A narrative structure comprises narrative portions that have a defined order. Signature analysis of known content that follows the narrative structure is used to train machine learning models for the narrative structure and the narrative portions that make up the narrative structure. Signature analysis of candidate content segments, along with machine learning models for the narrative portions, are used to identify candidate content segments that match the respective narrative portions. A candidate playlist is generated of the identified candidate content segments in the defined order. In one embodiment, the machine learning model for the narrative structure is used to validate the generated playlist.