Automated Media Transition Detection for Sponsored Content Insertion

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Solution Overview

Problem

Manual review of media content items to identify transitions for inserting sponsored content is inefficient and costly, especially when dealing with a large volume of content.

Innovation Solution

Using machine learning techniques to process reference content and identify characteristics associated with transitions, allowing for automated identification of comparable characteristics in new content items, enabling the insertion of sponsored content at optimal points without manual annotation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual review is used to identify transitions in media content items, then transition identification accuracy is improved, but processing time and cost increase significantly

Engineering Contradiction:

Improvetransition identification accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary processing of media content items by extracting audio and video features in advance, creating a structured representation that can be quickly analyzed for transitions. This preliminary feature extraction enables faster subsequent processing while maintaining accurate transition detection.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces manual mechanical review with an automated computational system that uses machine learning models to detect transitions. The system substitutes human reviewers with algorithms that analyze audio-visual features, dramatically reducing processing time while maintaining or improving detection accuracy through consistent application of detection criteria.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If manual review is used to identify transitions, then transition characteristics are accurately identified, but processing cost increases

Engineering Contradiction:

Improvetransition characteristics identification accuracyVSAvoidprocessing cost

Core Design Contradiction:

Measurement precisionVSLoss of energy

Solution Approach 1:

The system enables self-service processing where the media content item itself provides the information needed for transition detection through its own audio and video features. The content's inherent characteristics (audio changes, visual transitions) are used to identify transitions without requiring external manual analysis, making the process cost-effective and scalable.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

Manual review operations are replaced with automated machine learning-based detection systems that process media content at scale. The substitution of human labor with computational algorithms reduces per-unit processing costs while maintaining high accuracy in identifying transition characteristics through pattern recognition.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If automated processing is implemented to reduce manual review, then processing efficiency is improved, but transition identification accuracy may deteriorate

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidtransition identification accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system employs multiple adjustable parameters including audio feature thresholds, video change detection sensitivity, and machine learning model confidence levels. These parameters can be optimized and tuned to achieve the desired balance between processing efficiency and transition identification accuracy, allowing the automated system to adapt to different content types and transition characteristics.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent implements a multi-functional system that processes both audio and video features simultaneously using a unified machine learning framework. This universal approach enables the system to handle diverse media content types and transition patterns effectively, maintaining high accuracy across different scenarios while preserving processing efficiency through integrated analysis.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS10257546B1Identifying transitions within media content items

Publication Date: 2019.04.09 GOOGLE LLC

AI summary

Systems and methods are disclosed for identifying transitions within media content items. In one implementation, a processing device process a first media content item, the first media content item being associated with a transition, to identify one or more characteristics associated with the transition. The processing device processes a second media content item to identify at least one of the one or more characteristics at a chronological interval of the second media content item. The processing device receives a sponsored content item. The processing device provides, during a presentation of the second media content item, the sponsored content item at the chronological interval.