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.

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50+Jurisdictions

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Evidence captured

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01Assess
02Identify
03Compare
04Execute
05Monitor
Target intelligenceWho to enforce against, and in what order
01Target A
94
Settlement: 73%Prior cases: 12
02Target B
81
Settlement: 65%Prior cases: 8
03Target C
67
Settlement: 48%Prior cases: 3
Strategy comparisonWhich enforcement approach produces the best outcome
Germany first
72%
14 monthsModerate
Multi-jurisdictionRecommended
91%
8 monthsHigher upfront cost
US district court
64%
18 monthsAppeal risk
Outcome predictionWhat will happen if you file, with confidence ranges
Settlement
82%
Trial
12%
Dismissal
6%
Expected duration9–14 mo
ConcentrationModerate
Based on resolved historical cases, judge patterns, and defendant behaviour, with duration and concentration for portfolio marks. Confidence narrows as the case progresses.
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