Conversational Interface Tokenization for Intent Classification
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Solution Overview
Problem
Conversational interfaces struggle to accurately determine user intent in closed-domain environments like cybersecurity due to the vast number of technical terms and proper nouns, leading to large and non-generalizable models that fail to perform accurately in production settings.
Innovation Solution
A system and method that involves tokenizing and redacting specific terms in an utterance to generate a simplified text string, allowing for intent classification based on labeled features rather than specific vocabulary, thereby reducing model complexity and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a prior art intent classifier uses a library containing the entire vocabulary that might conceivably be uttered by a user to capture diverse technical jargon and proper nouns, then the vocabulary coverage is improved, but the model size becomes extremely large and fails to generalize well outside the training environment
Solution Approach 1:
The patent extracts and removes specific vocabulary items (technical jargon and proper nouns) from the input text before processing. By taking out these diverse vocabulary elements and replacing them with generic labels, the system achieves broad vocabulary coverage without requiring a large model to memorize every possible term.
Solution Approach 2:
The patent changes the parameter of vocabulary representation from specific words to generic labels. Instead of using the original diverse vocabulary, the system transforms it into a standardized set of labeled categories, allowing the model to handle diverse input with a fixed, manageable parameter set.
2Adaptability or versatility
If a prior art intent classifier attempts to capture highly diverse vocabularies including technical jargon and proper nouns, then the vocabulary diversity is improved, but the model fails to produce the performance required in a production setting
Solution Approach 1:
The patent extracts diverse vocabulary elements and replaces them with standardized labels. This extraction process removes the variability that causes classification errors while preserving the semantic meaning needed for accurate intent determination, thereby improving reliability without sacrificing vocabulary diversity.
Solution Approach 2:
The patent creates a universal labeling system that can handle multiple types of diverse vocabulary (technical jargon, proper nouns, entity names) through a single set of generic labels. This universal approach allows the model to generalize across diverse vocabularies reliably without needing domain-specific adjustments.
3Ease of operation
If a prior art conversational interface uses supervised machine learning models with extensive vocabulary libraries to determine user intent in closed-domain environments, then the intent classification capability is improved, but the bandwidth requirements and computational resources increase significantly
Solution Approach 1:
The patent extracts and removes specific vocabulary before transmission or processing. By taking out the diverse vocabulary and replacing it with compact generic labels, the system reduces the amount of data that needs to be transmitted and processed, thereby reducing bandwidth consumption and computational energy requirements while maintaining intent classification capability.
Data Source
AI summary
An improved system and method is disclosed for receiving a spoken or written utterance, identifying and replacing certain words within the utterance with labels to generate a simplified text string representing the utterance, performing intent classification based on the simplified text string, and performing an action based on the intent classification and the original words that were replaced.