Corpus Quality Processing for Unstructured Documents
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computing environments face challenges in efficiently processing and enhancing the quality of unstructured electronic documents for specific tasks, such as machine learning, due to the complexity and variability of these documents.
Innovation Solution
A computer-implemented method utilizing a corpus processing system that assesses and enhances the quality of unstructured documents by referencing the documents, applying quality metrics, selecting task-relevant metrics, and transforming documents to remediate identified issues, thereby optimizing them for specific tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all quality metrics are applied to assess document quality, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the quality metrics into different categories (structural metrics, syntactic metrics, semantic metrics) and applies them hierarchically to documents. This segmentation allows comprehensive quality assessment while managing system complexity through organized modular processing of different metric types.
Solution Approach 2:
The patent applies different quality metrics selectively based on document characteristics and task requirements. Rather than uniformly applying all metrics to all documents, the system tailors metric application to local document needs, improving precision while reducing unnecessary processing complexity.
2Manufacturing precision
If comprehensive quality metrics are applied to all documents, then manufacturing precision is improved, but productivity decreases
Solution Approach 1:
The patent performs preliminary assessment of documents to identify quality issues before applying comprehensive quality metrics. This preliminary action allows the system to focus intensive processing only on documents that need it, improving quality enhancement while maintaining processing throughput by avoiding unnecessary comprehensive analysis of already-adequate documents.
Solution Approach 2:
The patent applies quality metrics selectively based on document needs and task requirements rather than uniformly to all documents. This partial action approach ensures sufficient quality enhancement for critical documents while reducing processing load overall, balancing manufacturing precision with productivity.
3Adaptability or versatility
If task-specific quality metrics are automatically selected, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic metric selection that adapts to different tasks and document types. The system automatically adjusts which quality metrics are applied based on the specific task requirements and document characteristics, improving adaptability while the automated selection process manages complexity through algorithmic decision-making rather than manual configuration.
Solution Approach 2:
The patent employs automated metric selection engines that self-determine the appropriate quality metrics based on task descriptions and document analysis. This self-service approach improves adaptability to different tasks while reducing the complexity burden on users, as the system autonomously configures the appropriate metric set without requiring manual intervention.
4Manufacturing precision
If automatic document transformation is applied to remediate issues, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary identification and prioritization of quality issues before applying automatic transformations. By assessing documents first and ranking issues by severity and task relevance, the system applies transformations only to critical problems, improving document quality while minimizing unnecessary processing time spent on minor issues.
Solution Approach 2:
The patent applies targeted transformations that modify specific document parameters to remediate identified quality issues. Rather than comprehensive reprocessing, the system makes precise parameter changes (such as formatting adjustments, structural corrections, or content modifications) only where needed, improving document quality efficiently while reducing overall transformation processing time.
Data Source
AI summary
Processing within a computing environment is facilitated using a corpus processing system to assess and enhance quality of a corpus of unstructured documents for a specified task. The processing includes referencing, by a corpus processing engine, the corpus of unstructured documents to obtain unstructured document data, and applying, by a corpus quality metrics engine, a set of quality metrics to the document data to obtain a set of quality metric scores. Further, the process includes automatically selecting, by a quality metric selection engine, a subset of task-relevant quality metrics using the quality metric scores and the specified task, and automatically transforming, at least in part, multiple documents of the corpus to remediate one or more identified issues with the documents. The automatically transforming results in remediated documents tuned for the specified task, which are provided for the specified task to be performed.