Conversational System Anomalous User Behavior Identification
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Solution Overview
Problem
Conversational systems face challenges in identifying and managing anomalous user behavior, which can include misuse or disruption, as existing methods lack effective mechanisms to differentiate between human and electronic users or to adapt interactions based on user behavior deviations.
Innovation Solution
The system evaluates user behavior through various factors such as conversation attributes, substantive attributes, and user attributes, generates a behavior measure by comparing these factors to an aggregated measure of a larger user group, and adapts its interactions to encourage, discourage, or further observe anomalous behavior by introducing challenges or modifying responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the conversational system processes all users uniformly, then the system operation is simple, but anomalous behavior cannot be effectively identified or managed
Solution Approach 1:
The system segments users into different categories based on their behavior patterns. It evaluates multiple factors (conversation attributes, substantive attributes, user attributes) to classify users as normal or anomalous, allowing differentiated processing for different user segments rather than uniform treatment of all users
Solution Approach 2:
The system changes the parameters of interaction based on user behavior classification. For anomalous users, it modifies conversation flow, introduces challenges, or adjusts response strategies. This dynamic parameter adjustment enables precise behavior identification while managing system complexity through conditional logic
2Reliability
If the system introduces challenges to discourage anomalous behavior, then user behavior can be better managed, but the ease of operation decreases
Solution Approach 1:
The system dynamically adjusts interaction difficulty based on real-time behavior assessment. Challenges are introduced adaptively for anomalous users rather than being static or universal. The conversation flow, response depth, and verification requirements change dynamically based on the user's behavior pattern, maintaining ease of operation for normal users while effectively managing anomalous behavior
Solution Approach 2:
The system uses an intermediary behavioral evaluation layer that sits between the user and the core conversational processing. This intermediary assesses behavior factors and mediates the interaction by selectively introducing challenges or modifications only when anomalous patterns are detected, preserving ease of operation for legitimate users
Data Source
AI summary
Examples of the present disclosure describe systems and methods relating to conversational system user behavior identification. A user of the conversational system may be evaluated based on one or more factors. The one or more factors may be compared to an aggregated measure for a larger group of conversational system users, such that “anomalous” behavior (e.g., behavior that deviates from a normal behavior) may be identified. When a user is identified as exhibiting anomalous behavior, the conversational system may adapt its interactions with the user in order to encourage, discourage, or further observe the identified behavior. As a result, the conversational system may be able to verify a user's anomalous behavior, discourage the anomalous behavior, or take other action while interacting with the user.