Document Key Phrase Characterization via Statistical Segmentation
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Solution Overview
Problem
The vast amount of available data makes it difficult for users to quickly identify relevant information, as existing methods lack efficient automated characterization techniques to concisely and informatively summarize document content.
Innovation Solution
The system automatically characterizes documents by generating a statistical model based on user input, segmenting document content, determining statistical significance, and displaying representative segments, utilizing special-purpose computing devices to perform these operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated characterization techniques are implemented to summarize document content, then information retrieval efficiency is improved, but system complexity increases
Solution Approach 1:
The patent segments document content into distinct units (segments) and identifies key phrases within those segments. This segmentation approach allows the system to process large volumes of data by breaking them into manageable portions, improving information retrieval efficiency without requiring the entire system to handle all data at once, thus managing complexity through modular processing.
Solution Approach 2:
The system extracts key phrases and statistically significant segments from documents to create concise characterizations. By taking out only the most relevant information rather than processing or presenting all data, the system improves retrieval efficiency while keeping the characterization output compact and manageable, effectively filtering information to reduce complexity.
2Measurement precision
If statistical analysis is applied to identify key phrases, then characterization accuracy is improved, but processing time increases
Solution Approach 1:
The patent uses statistical models to pre-identify segments and key phrases that are likely to be significant. By performing preliminary statistical analysis to flag promising segments before detailed characterization, the system improves accuracy by focusing computational resources on the most relevant portions of documents, thereby reducing overall processing time.
Solution Approach 2:
The system adjusts statistical parameters and thresholds to optimize the balance between accuracy and processing speed. By changing parameters such as significance thresholds and segment size criteria, the system can tune its operation to achieve adequate characterization accuracy while minimizing processing time based on specific needs.
Data Source
AI summary
Systems and methods are disclosed for key phrase characterization of documents. In accordance with one implementation, a method is provided for key phrase characterization of documents. The method includes obtaining a first plurality of documents based at least on a user input, obtaining a statistical model based at least on the user input, and obtaining, from content of the first plurality of documents, a plurality of segments. The method also includes determining statistical significance of the plurality of segments based at least on the statistical model and the content, and providing for display a representative segment from the plurality of segments, the representative segment being determined based at least on the statistical significance.