Predatory Behavior Detection via Multi-Stage Risk Scoring
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Solution Overview
Problem
Identifying predatory behavior in large-scale communication systems is challenging due to high false positives and erroneous data from simple keyword searches, posing risks to minors and system entities.
Innovation Solution
A system that queries textual messages with sets of key phrases to score conversations for risk of predatory behavior, using a multi-point risk system and SQIP scoring to determine conversation risk scores, and ranks potential threats for investigative prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If simple keyword searches are used to identify predatory behavior, then the detection process is simple and fast, but the result contains vast amounts of false positives and erroneous data
Solution Approach 1:
The patent segments the detection process into multiple stages: initial keyword filtering, followed by contextual analysis, relationship pattern recognition, and risk scoring. This multi-stage segmentation allows the system to maintain fast initial screening while progressively eliminating false positives through increasingly sophisticated analysis layers.
Solution Approach 2:
The system changes multiple parameters simultaneously: it transitions from single keyword matching to multi-parameter evaluation including contextual semantics, user relationship dynamics, conversation patterns, and temporal analysis. This parameter transformation enables the system to distinguish true predatory behavior from benign interactions that may contain similar keywords.
2Measurement precision
If comprehensive analysis is performed to reduce false positives, then identification accuracy improves, but the system complexity and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-computing user profiles, relationship graphs, and contextual metadata before actual detection occurs. Conversation histories, user behaviors, and interaction patterns are pre-analyzed and stored as structured data, enabling faster and more accurate real-time detection without repeating comprehensive analysis for every message.
Solution Approach 2:
The patent introduces intermediary components including contextual analysis modules, relationship graph processors, and risk scoring mechanisms that mediate between raw keyword matches and final identification results. These intermediaries process and filter data progressively, reducing the complexity burden on any single component while maintaining high overall accuracy.
3Measurement precision
If comprehensive analysis is performed to reduce false positives, then identification accuracy improves, but the time and computational resources required increase
Solution Approach 1:
The system employs dynamic analysis depth adjustment based on risk indicators. High-risk keywords or patterns trigger immediate comprehensive multi-factor analysis, while low-risk messages receive streamlined processing. This dynamic approach allocates computational resources proportionally to actual risk levels, maintaining high accuracy for critical cases while minimizing time loss for benign communications.
Solution Approach 2:
The system maintains continuous background processes that periodically update user profiles, relationship graphs, and contextual databases without interrupting the main detection workflow. This continuous background computation ensures that when detection is needed, the system already has current, accurate data ready, eliminating the need for time-consuming on-demand comprehensive analysis.
Data Source
AI summary
Methods, systems, apparatuses, and computer program products are provided that enable the identification of predatory behavior in communications systems. A plurality of textual messages of a message repository is queried with a plurality of sets of key phrases to determine and score textual messages that include one or more of the key phrases of the sets. Each scored textual message includes a suspect, a potential victim, and a score. Each suspect-to-potential victim pair corresponds to a conversation that includes the scored textual messages between the suspect and potential victim of the pair. A plurality of conversation risk scores is determined based at least on the scored textual messages. Each conversation risk score indicates an estimate of a risk of predatory behavior occurring during the corresponding conversation.