Dynamic Semantic Role Classification for IVR Intent Recognition
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Solution Overview
Problem
Interactive Voice Response (IVR) systems struggle to accurately identify the intent behind human inquiries, leading to suboptimal responses, especially when dealing with diverse industry categories.
Innovation Solution
A dynamic semantic role classification system is implemented within IVR systems, which receives and processes utterances by indexing tokens, selecting relevant tokens for classification, assigning semantic role classifiers, and using machine learning algorithms to update and refine these classifiers based on industry-specific data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional IVR systems use fixed classification rules, then system complexity is low, but classification accuracy and adaptability to diverse industry categories deteriorates
Solution Approach 1:
The patent implements dynamic semantic role classification that adapts to different industry categories. The system transitions from fixed classification rules to dynamic, context-aware classification by identifying semantic roles (topic, action, topic-modifier, question, negation) and adjusting classification behavior based on the detected industry category, thereby improving accuracy without requiring complete reconfiguration for each industry
Solution Approach 2:
The classification system is segmented into distinct semantic role classifiers that independently analyze different aspects of user input. Each semantic role (topic, action, topic-modifier, question, negation) is classified separately, allowing the system to handle diverse industry categories through modular, independent classification components rather than a monolithic complex system
2Measurement precision
If IVR systems use comprehensive semantic classification for all tokens, then classification coverage is complete, but processing time and computational resources increase
Solution Approach 1:
The system extracts and indexes only the most relevant tokens from user input for semantic classification. By identifying and focusing on key tokens that carry semantic meaning while excluding irrelevant tokens, the system achieves comprehensive classification coverage of important elements without the computational overhead of analyzing every single token in the input
Solution Approach 2:
The system applies semantic role classification selectively to tokens that benefit most from it, rather than uniformly classifying all tokens. This partial action approach concentrates computational resources on tokens where semantic classification provides the greatest value, reducing overall processing time while maintaining effective classification coverage
3Adaptability or versatility
If IVR systems use static classification models, then system simplicity is maintained, but adaptability to industry-specific queries deteriorates
Solution Approach 1:
The semantic role classification system serves multiple industry categories simultaneously through a universal framework. The same semantic role classifiers (topic, action, topic-modifier, question, negation) are applied across different industries, allowing the system to adapt to industry-specific queries without requiring separate classification systems for each industry
Solution Approach 2:
The system applies different classification strategies to different parts of the input based on local context. By detecting industry-specific keywords and patterns in specific regions of the input, the system adjusts its classification behavior locally for those segments while maintaining the overall universal framework, enabling industry-specific adaptability without complete system redesign
Data Source
AI summary
A system for dynamic semantic role classification, through an entity's natural language process (NLP) pipeline is provided. The system may include assigning semantic role classifiers to tokens included in utterances received from user nodes. The system may include using a machine learning algorithm to assign the semantic role classifiers. The machine learning algorithm may assign the semantic role classifiers based on a calculated correlation value. The machine learning algorithm may use training and testing data sets to dynamically update the semantic role classifiers.