Personalized Media Content Generation Using Segment Relevance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing methods for generating summarized media content, such as movie trailers, are slow and labor-intensive, requiring manual editing and lack personalization for users, making it difficult for content providers to engage users effectively in a crowded media landscape.
Innovation Solution
A method that involves obtaining media content, plot data, and user profile data to identify relevant segments, select a subset of segments based on relevance scores, and generate shorter, personalized media content, such as trailers, using a content generator system that includes a processor and data storage for efficient output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual editing methods are used to generate summarized media content, then the content can be customized and edited with high precision, but the process becomes slow and labor-intensive
Solution Approach 1:
The patent segments the media content into multiple segments and uses automated algorithms to identify and select relevant segments based on plot data and user profiles, replacing manual frame-by-frame editing with systematic segment-based processing that maintains precision while improving speed
Solution Approach 2:
The system enables self-service content generation by automatically analyzing media content, determining plot relevance, selecting segments based on user profiles, and generating personalized summaries without requiring manual intervention, thus dramatically improving productivity while maintaining acceptable precision through algorithmic selection
2Productivity
If generic summarized content is generated without personalization, then the generation process is faster and simpler, but user engagement decreases due to lack of personalization
Solution Approach 1:
The patent applies local quality by tailoring the summarized content to individual user preferences through user profile data, where different users receive different segment selections and content arrangements based on their specific interests, ensuring high adaptability while maintaining efficient automated generation
Solution Approach 2:
The system performs preliminary action by pre-processing media content into segments and pre-analyzing user profiles before generating the final personalized summary, allowing the system to quickly assemble personalized content without sacrificing generation speed through real-time processing
Data Source
AI summary
In one aspect, an example method includes (i) obtaining first media content; (ii) obtaining plot data associated with the obtained first media content; (iii) obtaining user profile data associated with a user; (iv) identifying from the obtained first media content, a first set of segments; (v) using at least the obtained plot data, the obtained user profile data, and segment data associated with each segment, to determine segment relevance data for each such segment; (vi) using at least the determined segment relevance data for the segments as a basis to select, from among the identified first set of segments, a second set of segments; (vii) using at least the selected second set of segments to generate second media content, wherein the generated second media content is shorter in duration than the obtained first media content; and (viii) outputting for presentation the generated second media content.