Real-Time Keystroke Sentiment Analysis for Compliance Enforcement
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Solution Overview
Problem
Existing compliance mechanisms in companies are ineffective in preventing non-compliant digital messages as they rely on employee self-policing and typically analyze communications after the fact, failing to prevent messages that violate company policies.
Innovation Solution
A system and method that monitor and analyze digital messages in real-time to identify potential compliance violations by parsing keystrokes, assigning scores to words, and comparing aggregated scores to a threshold, precluding non-compliant messages from being sent by suggesting modifications and disabling messaging functionality until compliance is met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time message monitoring and analysis is implemented, then compliance effectiveness is improved, but device complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary analysis of message content during the composition phase, assigning sentiment scores to words and calculating aggregate sentiment before the message is sent. This preliminary action allows compliance violations to be detected and prevented before transmission, improving reliability while managing complexity through early intervention rather than post-hoc analysis
Solution Approach 2:
The patent introduces an intermediary sentiment analysis layer between the user composing a message and the message transmission system. This intermediary component analyzes word sentiment scores, calculates aggregate sentiment, and determines compliance before allowing message sending, thereby improving compliance effectiveness without requiring complete system redesign
2Measurement precision
If comprehensive word scoring and sentiment analysis is performed, then measurement precision of message sentiment is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs sentiment analysis on a partial basis by focusing only on the sentiment component of message compliance rather than analyzing all linguistic aspects. It assigns scores to specific words and calculates aggregate sentiment scores, providing sufficient precision for compliance determination without the computational overhead of comprehensive linguistic analysis
Solution Approach 2:
The message analysis process is segmented into discrete word-level sentiment scoring operations. Each word is independently scored for sentiment, and these individual scores are aggregated to determine overall message sentiment. This segmentation allows for efficient parallel processing and reduces the computational complexity of analyzing entire messages as single units
3Reliability
If messaging functionality is disabled until compliance is met, then compliance enforcement is improved, but ease of operation deteriorates
Solution Approach 1:
The system provides immediate feedback to users about the sentiment compliance of their messages in real-time during composition. By displaying sentiment scores and compliance status as users type, the system enables users to adjust their messages to achieve compliance without completely blocking the messaging function, thereby maintaining ease of operation while ensuring compliance enforcement
Data Source
AI summary
This disclosure describes systems, methods, and apparatus for monitoring keystrokes and determining whether to pass those keystrokes to a target application, for instance, to prevent compliance violations. Keystrokes or other inputs can be analyzed to determine if a non-compliant message is to be sent to an application layer, for instance via a semantic checker that considers phrase templates and parts-of-speech tagged tokenized portions of a message. If a non-compliant message is identified, then the keystrokes, or at least a last keystroke is precluded from reaching the target application or from being stored in temporary or long-term memory.