Media Summarization via Viewer Interaction Analysis
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Solution Overview
Problem
Existing video summarization techniques face challenges in automatically selecting the most interesting parts of a video, as they rely on subjective human judgment and are labor-intensive, leading to inefficiencies in identifying relevant content within long videos.
Innovation Solution
A viewer-interaction analyzer scores media segments based on viewer interactions, forming feature vectors to train a model that generates interest ratings, allowing for the automatic generation of summarized media files by retaining only segments meeting specific criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual human judgment is used to select interesting video segments, then summarization quality is improved, but labor intensity and time consumption increase
Solution Approach 1:
The system enables videos to summarize themselves by automatically analyzing viewer interaction data (clicks, watch time, likes) to identify interesting segments, eliminating the need for manual human review while maintaining objective quality standards based on actual viewer behavior
Solution Approach 2:
Manual human judgment is replaced with an automated computational model that processes viewer interaction metrics and trained machine learning algorithms to objectively score and select video segments, substituting mechanical human labor with an automated information processing system
2Measurement precision
If manual human judgment is used to select interesting video segments, then summarization quality is improved, but labor intensity increases
Solution Approach 1:
The summarization system performs self-service by automatically collecting viewer interaction data, training models on this data, and generating summaries without human intervention, completely automating the process that previously required manual human labor
Solution Approach 2:
The patent replaces manual human labor with an automated system that uses machine learning models trained on viewer interaction data to objectively evaluate and select video segments, achieving high summarization quality without human involvement
3Productivity
If automated summarization is implemented, then productivity is improved, but measurement precision may worsen
Solution Approach 1:
The system continuously improves summarization quality by collecting viewer interaction feedback (clicks, watch time, likes) on summarized videos, using this feedback to retrain and refine the automated models, creating a closed-loop system that enhances both productivity and measurement precision over time
Solution Approach 2:
The patent replaces manual human judgment with sophisticated automated machine learning models that process multiple viewer interaction metrics to objectively evaluate video segments, achieving high summarization quality through computational analysis rather than human review
Data Source
AI summary
Techniques for summarizing media are described. A viewer-interaction analyzer receives a media file containing media, the media file including a plurality of segments. A segment of the media file is scored based on interactions of a set of raters. Viewer metrics on the segment of the media file are measured based on interactions with the segment of the media file by a set of viewers. A set of feature vectors are formed based on the measured viewer interactions, where feature vectors in the set of feature vectors are based on interactions of the set of viewers. A model is trained based on the set of feature vectors and the score assigned to the segment of the media file. The model is applied to segments of the media file to generate an interest rating for segments of the media file. An edited media file is generated based on segments of the media file having interest ratings that meet a criterion. The Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.