Conversation Log Vulnerability Detection with Autonomous Remedial Actions
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Solution Overview
Problem
Existing online chat systems lack the ability to identify user vulnerabilities and automatically execute remedial actions to address these vulnerabilities, particularly in conversations where sensitive information is shared, which can prevent users from fulfilling obligations or accessing full service functionality.
Innovation Solution
A system utilizing a vulnerability detection system with modules for chunking, vulnerability identification, and remedial action execution, employing machine learning models to process conversation logs, identify vulnerabilities, and autonomously execute appropriate actions such as empathy, assistance, asset transfer, or teaching based on classified vulnerability states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If online chat systems provide specialized support for sensitive subject matter, then user support quality is improved, but system complexity increases due to need for vulnerability detection and automated remedial actions
Solution Approach 1:
The system automatically detects vulnerabilities and executes remedial actions without requiring manual intervention from support staff. The vulnerability detection system processes conversation logs, identifies vulnerability states, and triggers appropriate remedial actions autonomously, allowing the system to serve itself rather than requiring complex manual oversight for each case
Solution Approach 2:
An intermediary vulnerability detection system is introduced between the chat interface and the support response mechanism. This intermediary layer processes conversation logs, identifies vulnerabilities using machine learning models, and coordinates appropriate remedial actions, simplifying the overall system architecture by centralizing the detection and coordination function
2Measurement precision
If the system processes conversation logs to identify vulnerabilities, then vulnerability detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The conversation log is segmented into individual messages and processed in batches rather than analyzing the entire log at once. The vulnerability detection system processes conversation logs in manageable units, identifying vulnerability states in each segment and accumulating results, which improves processing speed while maintaining detection accuracy through comprehensive segment-by-segment analysis
Solution Approach 2:
The system performs preliminary processing of conversation logs by identifying and extracting relevant segments that may contain vulnerability indicators before performing comprehensive vulnerability analysis. This preliminary action filters and prioritizes data for deeper analysis, reducing overall processing time by focusing computational resources on the most promising segments
Data Source
AI summary
A system may include a processor and a non-transitory computer readable medium having stored thereon instructions that are executable by the processor to cause the system to receive a conversation log between a first user and a second user, derive, via a first machine learning model, at least one text chunk from the conversation log, process, via a second machine learning model, the at least one text chunk, the second machine learning model trained using previous conversation logs to determine whether the at least one text chunk indicates a vulnerability, in response to the at least one text chunk indicating the vulnerability, classify a type of the indicated vulnerability, and automatically execute a remedial action based on the classified type.