Automated Message Moderation Engine for Content Compliance

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Solution Overview

Problem

Traditional social media moderation techniques are inefficient, costly, and expose moderators to harmful content, leading to mental health issues and reputational damage due to the need for manual review of vast amounts of data.

Innovation Solution

A gesture-based moderation platform that automates the moderation of electronic messages and content using a message moderation engine, allowing for automatic disposition and predictive default actions, reducing the need for manual intervention and minimizing exposure to harmful content.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual moderation is used to review electronic messages, then content quality and community standards are maintained, but moderator exposure to harmful content increases and mental health deteriorates

Engineering Contradiction:

Improvecontent qualityVSAvoidmoderator exposure to harmful content

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

An automated moderation system acts as an intermediary between harmful content and human moderators. The system includes automated detection algorithms, machine learning models, and content filtering mechanisms that screen and flag problematic content before it reaches human reviewers, thereby protecting moderators while maintaining content quality standards.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The moderation process is segmented into multiple stages: automated preliminary screening, machine learning-based classification, and selective human review only for complex or ambiguous cases. This segmentation reduces the volume of harmful content that human moderators must directly handle while preserving quality control through layered review processes.

Inventive Principle:

Principle #1Segmentation

2Reliability

If manual moderation is used to review electronic messages, then content compliance is ensured, but time consumption and operational costs increase

Engineering Contradiction:

Improvecontent complianceVSAvoidmoderation time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The moderation system implements self-service capabilities through automated content analysis, where the system independently screens, classifies, and dispositions of compliant content without human intervention. Machine learning models automatically detect policy violations and execute appropriate actions, enabling the system to serve its own moderation needs while reserving human resources for edge cases.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically adjusts moderation parameters such as review thresholds, confidence levels, and disposition criteria based on content type, source, and historical data. This allows automated processing of clear-cut cases while maintaining high compliance standards, reducing time loss without compromising content compliance.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If automated moderation is implemented, then processing speed and efficiency increase, but complexity of the moderation system increases

Engineering Contradiction:

Improvemoderation speedVSAvoidmoderation system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The automated moderation system is designed as a multi-functional platform that handles various content types (text, images, video), multiple policy violations, and different disposition actions through a unified architecture. This universal design consolidates what would otherwise require multiple separate systems, managing complexity while maintaining high processing speed and productivity across diverse moderation scenarios.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Measurement precision

If manual moderation is used, then nuanced judgment and context understanding are applied, but scalability to handle increasing message volumes is limited

Engineering Contradiction:

Improvejudgment accuracyVSAvoidmessage processing capacity

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system performs preliminary automated analysis and classification of content before it reaches human moderators. Machine learning models pre-screen messages, identify clear violations, and prepare context summaries, enabling human reviewers to focus their nuanced judgment on complex cases only. This preliminary action maintains judgment accuracy while scaling processing capacity to handle increasing message volumes.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12120078B2Automated disposition of a community of electronic messages under moderation using a gesture-based computerized tool

Publication Date: 2024.10.15 KHOROS LLC

AI summary

Various embodiments relate generally to data science and data analysis, computer software and systems, and control systems to provide a platform to facilitate implementation of an interface as a computerized tool, among other things, and, more specifically, to a computing and data platform that implements logic to facilitate moderation of electronic messages, postings, content, etc., via implementation of a moderator application configured to, for example, perform one or more actions automatically, including disposition of an non-compliant electronic message. In some examples, a method may include activating at least a portion of a moderator application, decomposing an electronic message, accessing data representing disposition metrics, correlating data, and detecting that an electronic message is a non-compliant electronic message.