Automated Emote Content Pair Screening Using Neural Networks
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Solution Overview
Problem
The manual process of reviewing user-generated content for community standards in media distribution services is time-consuming and creates a backlog when content submission rates exceed review times, leading to inefficiencies in managing content submissions.
Innovation Solution
Implementing a neural network-based machine learning model that analyzes both images and text strings of emotes to determine their acceptability by generating content pair acceptability scores, allowing for automated evaluation and reduction of manual review burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review process is used to evaluate user-generated content, then content quality control is maintained, but review time increases and productivity decreases
Solution Approach 1:
An automated content evaluation system acts as an intermediary between content submission and manual review. The system includes training data preparation module, model training module, and content evaluation module that automatically assess user-generated content against community standards, filtering out clearly unacceptable content before it reaches manual reviewers.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated machine learning-based evaluation system. The system uses trained models to automatically evaluate content pairs (images and text), substituting human manual inspection with automated computational analysis to increase throughput while maintaining quality control.
2Measurement precision
If manual review process is used to ensure content standards, then content acceptability is determined accurately, but time consumption increases
Solution Approach 1:
The system performs preliminary automated evaluation of content before manual review. Training data is prepared in advance with labeled acceptability annotations, and models are trained beforehand to perform rapid automated assessment, so that only content requiring human judgment proceeds to manual review, reducing overall time loss.
Solution Approach 2:
The patent creates a computational model that copies and learns from human judgment patterns embedded in training data. The machine learning model is trained on labeled examples of acceptable and unacceptable content, enabling it to replicate human evaluation accuracy automatically without requiring actual human reviewers for every content item.
3Productivity
If automated machine learning model is implemented, then review efficiency increases, but system complexity increases
Solution Approach 1:
The automated evaluation system is segmented into distinct functional modules: training data preparation module, model training module, and content evaluation module. This segmentation allows each component to be developed, maintained, and scaled independently, managing system complexity while maintaining high review efficiency.
Solution Approach 2:
The trained machine learning model serves multiple functions: it evaluates both images and text strings, can be applied to various types of user-generated content, and provides consistent evaluation across different content pairs. This multi-functionality increases productivity without proportionally increasing complexity.
Data Source
AI summary
An emote management system receives a request to make an emote available for users of an application for inclusion in a real-time content stream provided by the application, wherein the emote includes a proposed content pair comprising an image and a text string. The emote management system determines, based on application of a machine learning model to the content pair, whether the proposed content pair satisfies a content pair acceptability threshold. Based a determination that the proposed content pair satisfies the content pair acceptability threshold, return a response to the client indicating that the proposed content pair is an accepted content pair.