Dialogue System Intent Prediction via Profile Anomaly Detection
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Solution Overview
Problem
Automated dialogue systems often struggle with ambiguous user inputs, leading to inaccurate or non-optimized responses due to inability to disambiguate references, resulting in user frustration.
Innovation Solution
The system predicts user intent based on anomalies in user profile data, using fuzzy logic to disambiguate ambiguous terms and generate accurate responses by analyzing historical dialogue sessions and user profile changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the automated dialogue system processes user inputs literally without disambiguation, then the system operation is simple, but the response accuracy deteriorates
Solution Approach 1:
The system performs preliminary analysis of user profile data and historical dialogue patterns before processing the ambiguous input. By pre-computing user preferences, behavior patterns, and context information, the system prepares disambiguation rules in advance that enable accurate interpretation of ambiguous terms without adding complexity to the real-time processing flow
Solution Approach 2:
The system introduces an intermediary disambiguation module that acts as a mediator between the simple input processing and the accurate response generation. This module uses fuzzy logic to interpret ambiguous terms by comparing them against pre-analyzed user profile data and historical patterns, thereby achieving accurate responses while maintaining overall system simplicity
2Loss of information
If the system ignores ambiguous portions of user input, then the processing speed is fast, but the information completeness deteriorates
Solution Approach 1:
The system applies partial disambiguation action by focusing only on the ambiguous portions of user input that require interpretation, rather than reprocessing the entire input. The fuzzy logic module selectively analyzes ambiguous terms using pre-computed user profile data, achieving complete information recovery without the overhead of complete reprocessing
Solution Approach 2:
The system uses self-service by leveraging pre-stored user profile data and historical dialogue patterns that automatically provide context for disambiguation. The fuzzy logic module queries these self-maintained data structures to resolve ambiguities without requiring external intervention or complex real-time analysis, thereby maintaining fast processing speed while achieving information completeness
Data Source
AI summary
Mechanisms are provided for customizing responses to future questions based on identified anomalies in user profile information. An automated dialogue system monitors information associated with a plurality of entities, where the information includes quantities for variable values associated with the entities. The automated dialogue system, in response to determining that a quantity of a variable value associated with an entity in the plurality of entities has changed by an amount equal to or exceeding a corresponding threshold value, generates response information associated with a quantity of the variable value and an entity to respond to at least one future question. In addition, the automated dialogue system stores the responsive information in association with the entity for later retrieval in response to initiation of a dialogue session with the automated dialogue system. Time thresholds may be established for determining when to stop using the responsive information for responding to questions.