Multi-Pipeline NLP Platform for Intent Resolution
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Solution Overview
Problem
Conventional language processing systems face speed, accuracy, and efficiency deficiencies in disambiguating spoken utterances, often leading to slow and inaccurate results due to time-consuming queries to resolve the intent behind words with multiple meanings.
Innovation Solution
A multi-pipeline digital platform that uses a processor, database with distinct rule sets containing regular expression patterns, and a natural language processing engine to quickly tokenize and annotate utterances, determine the appropriate pipeline, and match the utterance to resolve the intent, thereby increasing processing speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional language processing systems use single pipeline processing with disambiguation queries, then accuracy may be maintained through user clarification, but processing speed deteriorates due to time-consuming user interactions
Solution Approach 1:
The system segments the language processing into multiple parallel pipelines, each dedicated to specific intent categories. This allows simultaneous processing of different utterance types without mutual interference, dramatically improving processing speed while maintaining accuracy through specialized pattern matching in each pipeline
Solution Approach 2:
The system performs preliminary classification of utterances into specific pipelines before detailed intent analysis. By pre-categorizing utterances based on initial keywords and patterns, the system routes them to appropriate specialized pipelines, eliminating the need for time-consuming disambiguation queries while maintaining high accuracy
2Measurement precision
If conventional systems perform comprehensive analysis to ensure accurate intent recognition, then accuracy is improved, but processing efficiency deteriorates due to redundant processing steps
Solution Approach 1:
Each pipeline is configured with specialized rule sets and regular expression patterns tailored to specific intent categories. This local optimization allows each pipeline to process its designated utterance types with high efficiency and accuracy, avoiding the redundant comprehensive analysis required by single-pipeline systems
Solution Approach 2:
The system performs preliminary routing of utterances to appropriate pipelines based on initial analysis. This pre-sorting mechanism ensures that each utterance is processed by the most suitable specialized pipeline from the start, eliminating redundant processing steps and improving overall efficiency while maintaining accuracy
3Measurement precision
If conventional systems use single rule set for all utterances, then system complexity is reduced, but accuracy deteriorates due to inability to handle diverse intent patterns
Solution Approach 1:
The system segments the rule sets into multiple distinct collections, each optimized for specific intent categories. This segmentation allows each rule set to be specialized for its domain, improving accuracy through targeted pattern matching while organizing complexity into manageable, independent modules
Solution Approach 2:
The multi-pipeline architecture provides a universal framework that can handle diverse intent patterns across multiple domains. Each pipeline within the system is specialized for specific patterns, yet the overall system maintains universality by accommodating various intent types through its modular pipeline structure
Data Source
AI summary
Aspects of the disclosure relate to systems and methods for increasing the speed, accuracy, and efficiency of language processing systems. A provided method may include storing a plurality of distinct rule sets in a database. Each of the rule sets may be associated with a different pipeline from a set of pipelines. The method may include receiving the utterance. The method may include tokenizing and/or annotating the utterance, determining a pipeline for the utterance, and comparing the utterance to the rule set that is associated with the pipeline. When a match is achieved between the utterance and the rule set, the method may include resolving the intent of the utterance based on the match. The method may include transmitting a request corresponding to the intent to a central server, receiving a response, and transmitting the response to the system user.