Bot Management Module for Chatbot Interaction Filtering
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Solution Overview
Problem
Current systems face challenges in distinguishing and managing chatbot-generated interactions, which can lead to resource wastage, data skew, and potential system attacks, as chatbots can emulate human responses and overwhelm feedback acquisition systems.
Innovation Solution
A system with a bot management module that engages in dialog with users, evaluates interactions to determine if they are human or chatbot-generated, and prevents further processing of chatbot-derived content, using blacklists, response rate thresholds, and machine learning algorithms to differentiate between human and chatbot interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system processes all interactions without discrimination, then all user feedback is captured, but system resources are wasted on chatbot-generated content and data accuracy deteriorates
Solution Approach 1:
The bot management module performs preliminary detection and classification of interactions as human or chatbot-generated before the main processing pipeline. This preliminary action filters out chatbot content early, preventing wasteful processing of non-human interactions while preserving all genuine user feedback for accurate analysis.
Solution Approach 2:
The bot management module acts as an intermediary layer between the interaction input and the main processing system. It detects and classifies chatbot-generated interactions, then selectively routes only human-generated content to the feedback processing pipeline, thereby eliminating resource wastage while maintaining data accuracy.
2Productivity
If chatbot interactions are allowed to proceed normally, then automated service efficiency is improved, but system reliability deteriorates due to potential attacks and denial of service
Solution Approach 1:
The system applies preliminary anti-action by detecting and blocking malicious chatbot interactions before they can cause harm. The bot management module identifies patterns indicative of attacks or denial of service attempts and prevents these interactions from reaching the main system, thereby protecting reliability while allowing legitimate automated services to continue.
Solution Approach 2:
The system converts the harmful presence of malicious chatbots into a benefit by using their interaction patterns as training data for improved detection algorithms. The bot management module learns from adversarial interactions to better distinguish between legitimate automated services and malicious bots, thereby enhancing system reliability while maintaining productivity.
3Quantity of substance
If response rate thresholds are lowered to capture more feedback, then feedback quantity increases, but measurement precision deteriorates due to inclusion of chatbot responses
Solution Approach 1:
The bot management module performs preliminary classification of all incoming interactions as human or chatbot-generated before they are counted or analyzed. This preliminary action enables the system to accept all interactions (maintaining high feedback quantity) while filtering out chatbot responses (preserving feedback quality) in the subsequent processing stages.
Solution Approach 2:
The system segments the feedback stream into two distinct channels: human-generated feedback and chatbot-generated interactions. The bot management module separates these streams, allowing the system to process and count all feedback while ensuring that only human-generated content enters the quality analysis pipeline, thereby maintaining both quantity and precision.
Data Source
AI summary
Embodiments discussed herein refer to systems and methods for chatbot interactions. When chatbot derived interactions are detected, the system can prevent those interactions from being further processed. This can be performed by an analysis system operative to engage in a dialog with customers. The system can manage a dialog with a first customer and evaluate the dialog to determine whether any interactions or responses are associated with a chatbot or a human. Interactions or responses determined be associated with a chatbot are dropped and not permitted to be further processed by the analysis system.