Automated Content Curation via Narrative Structure Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The abundance of online content, particularly short-form content segments, makes it challenging to maintain user engagement due to lack of cohesiveness and narrative structure, leading to disinterest as users navigate disparate content pieces.
Innovation Solution
An automated content curation system using machine learning models analyzes segment signature vectors to identify and assemble content segments following a narrative structure, generating playlists that maintain user interest by ensuring coherency and consistency across segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content segments are curated without narrative structure, then content variety and availability are improved, but user engagement and cohesiveness deteriorate
Solution Approach 1:
The system performs preliminary analysis of content segments to identify and extract narrative structures before curation. Machine learning models are trained in advance to recognize narrative patterns, character arcs, and story elements, enabling the system to pre-organize content segments into coherent narrative sequences rather than randomly assembling them, thus maintaining user engagement while preserving content variety
Solution Approach 2:
The system changes the organizational parameters of content curation by transitioning from random or category-based assembly to narrative-structure-based assembly. By applying narrative structure parameters (such as story arcs, character development, and plot progression) as the primary organizing principle, the system maintains cohesiveness and user engagement while still incorporating diverse content segments
2Productivity
If content segments are assembled without narrative structure, then curation speed and simplicity are improved, but cohesiveness and user interest deteriorate
Solution Approach 1:
The system implements self-service automation where machine learning models automatically analyze, evaluate, and assemble content segments into narrative structures without human intervention. The models autonomously identify narrative patterns, match segments to appropriate narrative positions, and generate coherent content sequences, thereby maintaining both high curation speed and strong cohesiveness through automated intelligent processing
3Productivity
If automated systems are used for content curation, then efficiency and scalability are improved, but complexity of the system increases
Solution Approach 1:
The system introduces machine learning models as intermediary components that bridge the gap between raw content segments and curated narrative sequences. These models serve as intelligent mediators that automatically perform complex analysis and decision-making tasks, handling the system complexity internally while presenting a simple, efficient interface for content curation and generation
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.