NLP Analysis for Technical Document Anti-Pattern Detection
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Solution Overview
Problem
Information retrieval from technical documents is often an inexact process, especially in large datasets, as existing methods fail to effectively identify key technical points and anti-patterns, leading to suboptimal search results and potential pitfalls in documents like patent applications and contracts.
Innovation Solution
A computer-implemented method using natural language analysis and machine learning classifiers to classify sentences within input queries, identify key technical points, and detect anti-patterns within a corpus of documents, generating reports that highlight potential issues and relevant documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional information retrieval methods are used on large datasets, then search coverage is maintained, but measurement precision of key technical points deteriorates
Solution Approach 1:
The patent segments the document analysis process into distinct stages: sentence-level classification to identify key technical sentences, extraction of key technical points from those sentences, and anti-pattern detection on the extracted points. This segmentation enables precise analysis of key technical elements even in large corpora by focusing computational resources on relevant portions rather than processing entire documents uniformly.
Solution Approach 2:
The patent extracts key technical points from classified sentences as a separate, focused data layer. By taking out only the essential technical information from large volumes of text and storing it in a structured format, the system achieves high measurement precision for technical point identification while managing large corpus sizes efficiently through selective extraction rather than full-text analysis.
2Reliability
If comprehensive document analysis is performed, then reliability of search results improves, but loss of time increases
Solution Approach 1:
The patent performs preliminary sentence classification to identify key technical sentences before conducting full anti-pattern analysis. By pre-classifying sentences and extracting only key technical points in advance, the system prepares data in a structured format that enables faster, more reliable search results without requiring comprehensive analysis of entire documents during the search phase, thus reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent replaces traditional mechanical text search methods with machine learning-based sentence classification and key point extraction. This substitution enables the system to achieve comprehensive document analysis with improved reliability by using intelligent classification algorithms, while simultaneously reducing processing time by focusing analysis only on classified key sentences rather than scanning entire documents.
3Object-affected harmful factors
If detailed anti-pattern detection is implemented, then object-affected harmful factors are reduced, but device complexity increases
Solution Approach 1:
The patent extracts key technical points as a separate, structured data layer from the full text. By taking out only the essential technical information and performing anti-pattern detection on this extracted subset rather than the entire document corpus, the system effectively reduces harmful anti-patterns' impact on search quality while keeping the analysis system relatively simple and focused on critical elements only.
Solution Approach 2:
The patent applies different analysis depths to different parts of the document: sentence-level classification for structural understanding, key point extraction for essential information, and anti-pattern detection specifically on extracted key points. This local quality approach ensures thorough anti-pattern detection where it matters most (in key technical points) while avoiding unnecessary complexity in analyzing less critical portions of documents.
Data Source
AI summary
Embodiments of the present invention provide techniques for analyzing technical documents against known art. One or more machine learning classifiers are trained by a training set. References are ingested and assembled into a corpus. An input query is processed by natural language processing, and the classifiers are used to retrieve relevant documents and references, providing solutions to the input query. Anti-patterns are identified and provided to a user in the form of a report to alert the user to potential problems with the input query.