Proactive Media Matching via Automatic Reference Selection

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Solution Overview

Problem

Existing content sharing platforms rely on reactive models that require manual user input for identifying and managing reference media items, leading to inefficiencies and inaccuracies in detecting matching media items, especially for users without dedicated resources.

Innovation Solution

A proactive system that automatically identifies media items as reference items based on properties and content analysis, comparing them to previously uploaded items to detect matching media items without user input, using a qualification score and threshold-based matching to provide a graphical user interface for user actions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual user input is required for identifying reference media items, then user control over media item selection is improved, but system complexity and user effort increase

Engineering Contradiction:

Improveuser controlVSAvoidsystem complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system automatically identifies candidate reference media items by analyzing media properties, channel characteristics, and upload patterns without requiring manual user input. The processing device performs self-service by autonomously selecting reference media items based on predefined criteria including media item properties (duration, view count, engagement metrics) and channel properties (subscriber count, activity level), thereby eliminating the need for users to manually designate reference items while maintaining system control

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary analysis of media items to identify candidates for reference media item status before actual matching operations begin. By pre-identifying and pre-processing potential reference media items based on their properties and performance metrics, the system prepares the groundwork for subsequent matching operations, reducing the complexity of real-time decision-making and improving overall operational efficiency

Inventive Principle:

Principle #10Preliminary action

2Productivity

If proactive automatic detection is implemented, then detection efficiency is improved, but computing resource consumption increases

Engineering Contradiction:

Improvedetection efficiencyVSAvoidcomputing resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by focusing computational resources only on media items that meet specific candidacy criteria rather than analyzing all uploaded media items uniformly. By applying filters based on media item properties (duration thresholds, view count minimums) and channel properties (subscriber count, activity level), the system processes only a subset of media items that are most likely to be reference media items, thereby improving detection efficiency while controlling computing resource consumption

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system applies different analysis depths and computational approaches to different media items based on their local characteristics. Media items with higher engagement metrics, longer durations, or from more active channels receive more thorough analysis, while others receive streamlined processing. This localized quality approach optimizes computing resource allocation by concentrating resources where they are most needed for accurate detection

Inventive Principle:

Principle #3Local quality

3Measurement precision

If frame-by-frame content analysis is performed, then matching accuracy is improved, but processing time increases

Engineering Contradiction:

Improvematching accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system segments the content analysis process into distinct stages: initial filtering based on metadata and properties, intermediate analysis of key frames or sampled frames, and detailed frame-by-frame comparison only for candidate matches. This segmentation allows the system to achieve high matching accuracy for potential duplicates while minimizing overall processing time by avoiding exhaustive frame-by-frame analysis of all uploaded media items

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces intermediary processing steps between initial upload and final matching, including pre-processing to extract key features, creation of condensed representations or fingerprints of media content, and intermediate filtering layers. These intermediaries enable accurate matching by providing efficient comparison mechanisms that reduce the computational burden of direct frame-by-frame analysis while maintaining detection precision

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11722730B2Proactive detection of media item matching

Publication Date: 2023.08.08 GOOGLE LLC

AI summary

A system and method for detection of media items matching is disclosed. A method may include determining that a first media item associated with a media item owner is to be used as a reference media item to detect other media items matching the reference media item, detecting a subsequently uploaded media item that includes at least a threshold portion of the reference media item, and providing a graphical user interface (GUI) for presentation to the media item owner, the GUI including a media identifier associated with the subsequently uploaded media item and one or more actions to be initiated by the media item owner with respect to the subsequently uploaded media item.