Automated Document Quality Scoring via ML Heuristics
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Solution Overview
Problem
The manual process of document integration into systems is slow, expensive, and prone to errors due to varying document formats and structures, making it inefficient and inaccurate.
Innovation Solution
A method using machine learning models to determine the quality of documents by processing predefined attributes, generating quality scores, and selectively processing documents based on these scores, thereby automating the integration process and improving efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual document integration is used to ensure quality, then accuracy is improved, but productivity deteriorates
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated machine learning-based quality assessment system. The system uses trained models to evaluate document quality attributes automatically, eliminating the need for human reviewers while maintaining or improving assessment accuracy and dramatically increasing processing speed.
Solution Approach 2:
The system enables documents to be self-assessed for quality through automated attribute evaluation. The machine learning models independently determine quality scores without external human intervention, allowing the integration process to serve itself rather than relying on manual quality control.
2Reliability
If manual quality review is performed on each document, then reliability is improved, but loss of time worsens
Solution Approach 1:
The system performs preliminary quality assessment automatically before documents enter the main processing pipeline. By pre-evaluating quality attributes and filtering documents based on automated scores, the system ensures reliable quality control without adding time to the overall process, as the assessment occurs in parallel with or before subsequent processing steps.
Solution Approach 2:
Manual quality review is replaced with automated machine learning-based assessment that operates rapidly without human time constraints. The system maintains reliability through consistent application of trained models while eliminating the time loss associated with sequential manual review of each document.
3Productivity
If automated quality assessment is implemented, then productivity is improved, but measurement precision may deteriorate
Solution Approach 1:
The system performs preliminary training of machine learning models using labeled data to establish accurate quality assessment criteria before deployment. This preliminary action ensures that the automated system learns from examples of high and low quality documents, enabling it to make precise measurements at scale without sacrificing accuracy for speed.
Solution Approach 2:
The system incorporates feedback mechanisms where quality assessment results are continuously evaluated and used to refine the machine learning models. By feeding back actual outcomes and adjusting the models accordingly, the system maintains or improves measurement precision while operating at high productivity levels.
Data Source
AI summary
Techniques for cognitive document quality determination and automated heuristic generation are provided. A plurality of documents is received, where each of the plurality of documents contains natural language text. A plurality of values is determined for a first plurality of predefined attributes of the plurality of documents. A plurality of quality scores is generated for the plurality of documents by processing the plurality of values using a machine learning model, where the plurality of quality scores indicate a suitability of each of the plurality of documents to be processed using a target processing operation. A subset of documents is identified from the plurality of documents having respective quality scores below a predefined threshold. The subset of documents is flagged for further processing. At least one document of the plurality of documents that is not flagged is selectively processed using the target processing operation.