Claim 1 [preamble]
A computer-implemented method for content moderation comprising:Claim 1.1
receiving, by one or more processors, a content item submitted to an online platform;Claim 1.2
extracting, by the one or more processors, a plurality of features from the content item using a trained machine learning model;Claim 1.3
generating, by the one or more processors, a classification score based on the extracted features;Claim 1.4
comparing the classification score to a predetermined threshold value;Claim 1.5
in response to the classification score exceeding the threshold, automatically flagging the content item for review.Claim 2
The method of claim 1, wherein the trained machine learning model comprises a convolutional neural network.Claim 3
The method of claim 1, wherein extracting the plurality of features comprises identifying semantic embeddings.Claim 4
The method of claim 1, further comprising storing flagged content items in a review queue database.Claim 5
The method of claim 1, wherein the predetermined threshold is dynamically adjusted based on historical accuracy metrics.Evidence: Source Code Analysis
ContentModerationService.java, lines 142-156: processSubmission() method receives content via API endpoint and initiates classification pipeline.Evidence: Technical Documentation
Architecture Doc v2.3, Section 4.1: "The feature extraction module utilises a pre-trained transformer model to generate 768-dimensional embeddings."Evidence: Source Code Analysis
ClassifierModel.py, lines 89-102: forward() method implements scoring logic using extracted feature vectors.Evidence: Configuration Files
moderation_config.yaml: threshold_score: 0.85, action_on_exceed: "FLAG_FOR_REVIEW"Evidence: API Response Logs
Sample response showing automatic flagging when score > threshold: {"status": "flagged", "score": 0.91, "review_queue": "priority"}Evidence: Model Architecture
model_spec.json confirms ResNet-50 backbone with custom classification head, consistent with CNN architecture.Evidence: Database Schema
review_queue table schema includes flagged_content_id, timestamp, priority_score columns matching claim requirements.Evidence: Configuration Logic
ThresholdManager.java implements adaptive threshold adjustment based on precision/recall metrics from validation set.Evidence: Embedding Generation
EmbeddingService.java, lines 45-78: generateSemanticEmbeddings() uses sentence-transformers to create 768-dimensional vectors.Evidence: Prior Art Reference
US Patent 9,123,456, Fig. 3: Similar CNN architecture for image classification disclosed in 2015.Claim 1 [preamble]
A computer-implemented method for content moderation comprising:Claim 1.1
receiving, by one or more processors, a content item submitted to an online platform;Claim 1.2
extracting, by the one or more processors, a plurality of features from the content item using a trained machine learning model;Claim 1.3
generating, by the one or more processors, a classification score based on the extracted features;Claim 1.4
comparing the classification score to a predetermined threshold value;Claim 1.5
in response to the classification score exceeding the threshold, automatically flagging the content item for review.Claim 2
The method of claim 1, wherein the trained machine learning model comprises a convolutional neural network.Claim 3
The method of claim 1, wherein extracting the plurality of features comprises identifying semantic embeddings.Claim 4
The method of claim 1, further comprising storing flagged content items in a review queue database.Claim 5
The method of claim 1, wherein the predetermined threshold is dynamically adjusted based on historical accuracy metrics.Evidence: Source Code Analysis
ContentModerationService.java, lines 142-156: processSubmission() method receives content via API endpoint and initiates classification pipeline.Evidence: Technical Documentation
Architecture Doc v2.3, Section 4.1: "The feature extraction module utilises a pre-trained transformer model to generate 768-dimensional embeddings."Evidence: Source Code Analysis
ClassifierModel.py, lines 89-102: forward() method implements scoring logic using extracted feature vectors.Evidence: Configuration Files
moderation_config.yaml: threshold_score: 0.85, action_on_exceed: "FLAG_FOR_REVIEW"Evidence: API Response Logs
Sample response showing automatic flagging when score > threshold: {"status": "flagged", "score": 0.91, "review_queue": "priority"}Evidence: Model Architecture
model_spec.json confirms ResNet-50 backbone with custom classification head, consistent with CNN architecture.Evidence: Database Schema
review_queue table schema includes flagged_content_id, timestamp, priority_score columns matching claim requirements.Evidence: Configuration Logic
ThresholdManager.java implements adaptive threshold adjustment based on precision/recall metrics from validation set.Evidence: Embedding Generation
EmbeddingService.java, lines 45-78: generateSemanticEmbeddings() uses sentence-transformers to create 768-dimensional vectors.Evidence: Prior Art Reference
US Patent 9,123,456, Fig. 3: Similar CNN architecture for image classification disclosed in 2015.Run a screen
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