Phrase Extraction System for Vehicle Information Analysis
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Solution Overview
Problem
Existing text analysis systems face inefficiencies in processing vehicle information, leading to a high volume of irrelevant or non-useful phrases, which increases processing time and memory-intensive computing requirements.
Innovation Solution
A method and system for extracting relevant phrases from vehicle information by tokenizing text, building n-gram phrases using a seed list, and filtering them using external, internal, and context pattern relevance factors to identify relevant or irrelevant phrases, reducing the search space and computational intensity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text analysis methods are used to process vehicle information, then comprehensive phrase extraction is achieved, but processing time and memory requirements increase significantly
Solution Approach 1:
The patent segments the text processing task into distinct phases: tokenization, n-gram generation, and filtering. By dividing the comprehensive text analysis into smaller manageable segments with specific filters at each stage, the system achieves thorough phrase extraction while controlling processing time through incremental refinement rather than monolithic analysis.
Solution Approach 2:
The patent applies preliminary filtering actions before complete analysis. Seed lists are pre-generated based on domain knowledge, and multiple filtering criteria (frequency thresholds, n-gram length constraints) are established in advance. This preliminary structuring enables efficient processing by pre-defining what constitutes relevant phrases before the actual analysis begins.
2Measurement precision
If traditional text analysis methods are used to process vehicle information, then all phrases are analyzed, but memory-intensive computing requirements increase
Solution Approach 1:
The patent extracts and removes irrelevant information at multiple stages. Frequency filters extract and eliminate common non-informative terms, seed lists extract only domain-relevant phrases, and the filtering process continuously removes phrases that don't meet criteria. This progressive extraction of irrelevant data reduces memory consumption while preserving analysis thoroughness for remaining candidates.
Solution Approach 2:
The patent applies different quality standards and filtering criteria to different parts of the processing pipeline. Early stages use coarse filters (frequency thresholds, length constraints), while later stages apply more refined criteria (seed matching, domain-specific rules). This localized application of varying filter strengths optimizes memory usage by being stringent where needed and permissive where appropriate.
3Measurement precision
If comprehensive phrase extraction is performed without filtering, then all potential phrases are captured, but the volume of irrelevant phrases increases
Solution Approach 1:
The patent performs preliminary structuring by creating seed lists that encode domain knowledge about relevant vehicle phrases before analysis begins. These pre-defined seeds act as a framework that guides the extraction process, ensuring that comprehensive capture of potential phrases occurs within a structured context that inherently filters out obviously irrelevant content.
Solution Approach 2:
The patent implements feedback mechanisms where filtering results inform subsequent processing. Frequency analysis feedback identifies common terms for exclusion, seed matching feedback refines phrase selection, and iterative filtering adjusts criteria based on what is captured and eliminated. This feedback loop maintains comprehensive capture while continuously improving the signal-to-noise ratio through learned patterns.
Data Source
AI summary
A system and method for extracting a relevant phrase from text. The system and method may build a plurality of n-gram phrases using a seed from a seed list as a start, a middle, or an end of each n-gram phrase. The seed list may be directed to a specific vehicle system and each seed may indicate a symptom, part, or action to extract relevant phrases from vehicle information verbatims. The plurality of n-gram phrases may be filtered to obtain one or more relevant phrases. The filtering process may include calculating an external relevance factor, an internal relevance factor, or a context pattern relevance factor.