Match Graph Bayesian Review Prediction for Media Segments

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Solution Overview

Problem

Existing media item review systems require significant resources and time for manual review of newly uploaded media items, regardless of their similarity to labeled items, leading to inefficiencies and potential inappropriate content playback.

Innovation Solution

Utilizing Bayesian inference to predict review decisions by generating segment and media item prediction values based on similarity to labeled media items, reducing the need for manual review and optimizing resource usage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual review is performed on all newly uploaded media items, then content appropriateness is ensured, but processing time and resource consumption increase significantly

Engineering Contradiction:

Improvecontent appropriatenessVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The media item is divided into multiple segments, and each segment is independently compared against segments of labeled media items. This segmentation allows the system to process only relevant portions of media items that require review, rather than manually reviewing entire items, thus reducing processing time while maintaining content appropriateness verification.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary automated analysis by comparing segments of newly uploaded media items against a database of labeled media items before manual review. This preliminary action identifies segments that likely require review based on similarity to previously labeled content, reducing the overall processing time by pre-filtering items that need human attention.

Inventive Principle:

Principle #10Preliminary action

2Reliability

If manual review is performed on all newly uploaded media items, then content appropriateness is ensured, but processor overhead and energy consumption increase

Engineering Contradiction:

Improvecontent appropriatenessVSAvoidprocessor overhead

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

By segmenting media items and comparing only relevant segments against labeled items, the system reduces the computational workload required for processing. This segmentation approach minimizes processor overhead by avoiding redundant comparisons of entire media items, thereby reducing energy consumption while maintaining content verification reliability.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system uses copies of segments from previously labeled media items to compare against newly uploaded segments. This copying approach allows efficient comparison without requiring full manual review of each new item, reducing processor overhead and energy consumption while ensuring content appropriateness through automated similarity matching.

Inventive Principle:

Principle #26Copying

3Measurement precision

If segment-level analysis is performed to improve prediction accuracy, then review decision precision increases, but system complexity increases

Engineering Contradiction:

Improvereview decision precisionVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system divides media items into segments and performs independent analysis on each segment, comparing them against segments of labeled items. This segmentation enables precise review decisions at the segment level while managing system complexity by breaking down the analysis into smaller, more manageable units rather than analyzing entire media items as monolithic entities.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system uses feedback from segment comparison results to adjust prediction values and refine review decisions. By incorporating feedback mechanisms that learn from segment-level analysis outcomes, the system improves measurement precision while managing complexity through iterative refinement rather than requiring overly complex initial system design.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12511559B2Using bayesian inference to predict review decisions in a match graph

Publication Date: 2025.12.30 GOOGLE LLC

AI summary

Implementations disclose methods and systems for using Bayesian inference to predict review decisions in a match graph. A method includes identifying a current media item to be processed; processing labeled media items to identify labeled media items that include at least one respective segment that is similar to one of segments of the current media item; for each of the segments of the current media item, generating a segment prediction value indicating a particular property associated with a corresponding segment of the current media item based on properties associated with respective labeled media items that each include a respective segment similar to the corresponding segment; calculating a media item prediction value for the current media item based on a generated segment prediction value of each of the segments of the current media item; and causing the current media item to be processed based on the calculated media item prediction value.