Automated Transition Classification for Binge Watching Content
Solution Overview
Problem
Current content playback interfaces for OTT media lack automated methods to identify transition times between subject and non-subject portions of episodic content, leading to frustration during binge watching, as they rely on crowdsourcing which is prone to human error and inefficiency.
Innovation Solution
An automated system that extracts frame images from a candidate segment time window, classifies them using machine learning models, and applies multi-level convolutional conversions and computational neural networks to determine precise transition times between content segments, enabling seamless navigation during binge watching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If crowdsourcing is used to identify transition times, then human feedback can be obtained, but the approach suffers from human error, fuzziness, and insufficient feedback for new or unpopular programs
Solution Approach 1:
The patent replaces the mechanical system of human crowdsourcing with an automated computer vision system that uses image processing and machine learning algorithms to identify transition times. The system automatically analyzes visual features of video frames (such as detecting credits sequences, scene changes, or episode boundaries) without requiring human intervention, thereby eliminating human error and fuzziness while maintaining high accuracy.
Solution Approach 2:
The system enables self-service by allowing the video content itself to provide the information needed for transition detection. The automated analysis extracts visual cues directly from the video frames, making the system independent of external human feedback. This self-contained approach ensures consistent performance across new or unpopular programs without relying on crowd participation.
2Productivity
If manual crowdsourcing is used, then transition times can be identified, but it requires sufficient incentives and suffers from wait times for crowd responses
Solution Approach 1:
The patent substitutes the slow mechanical process of collecting and processing human feedback with an automated computational system that processes video frames in real-time or near-real-time. The machine learning model can analyze thousands of frames per second, identifying transition points instantaneously without the delays inherent in crowd response collection and aggregation.
Solution Approach 2:
The system performs preliminary action by pre-processing and analyzing video content as it is being ingested or stored, rather than waiting for crowd feedback. The automated detection occurs proactively during content processing, making transition time identification available immediately without requiring subsequent human intervention or waiting for crowd responses.
3Measurement precision
If automated machine learning classification is used, then transition times can be identified quickly and accurately, but the system complexity increases with multi-level convolutional conversions and neural networks
Solution Approach 1:
The patent applies segmentation by breaking down the complex task of transition detection into distinct processing stages: frame extraction, feature detection, classification, and transition identification. Each stage is handled by specialized components of the machine learning system, allowing for modular design and optimization. This segmented approach manages complexity by organizing the processing pipeline into manageable, independent modules that can be developed and tuned separately.
Solution Approach 2:
The system uses intermediary elements such as feature extraction layers and intermediate classification stages that bridge the raw video input and the final transition time output. These intermediaries transform complex visual data into simplified representations that are easier to process, reducing the overall system complexity while maintaining high detection precision through progressive refinement.
Data Source
AI summary
Novel techniques are described for automated transition classification for binge watching of content. For example, a number of frame images is extracted from a candidate segment time window of content. The frame images can automatically be classified by a trained machine learning model into segment and non-segment classifications, and the classification results can be represented by a two-dimensional (2D) image. The 2D image can be run through a multi-level convolutional conversion to output a set of output images, and a serialized representation of the output images can be run through a trained computational neural network to generate a transition array, from which a candidate transition time can be derived (indicating a precise time at which the content transitions to the classified segment).