Feature Extraction Pipeline for Latent Status Detection in Dense Documents
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Solution Overview
Problem
Machine learning models, particularly deep learning models, require voluminous data with specific proportions of training data diversity to be effective, and they consume large computational resources, making them impractical for scenarios with limited training data and resources.
Innovation Solution
Implement a feature extraction processing pipeline to reduce the dimensionality of document data, using simpler machine learning models like decision trees, and generate synthetic training data to achieve effective predictions with limited data and reduced computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used to analyze dense document information, then prediction accuracy is improved, but computational resource consumption increases significantly
Solution Approach 1:
The patent segments the document processing task into distinct stages: OCR text extraction, feature extraction (identifying key entities and relationships), and classification. This segmentation allows simpler models to handle each stage, reducing overall computational requirements while maintaining accuracy.
Solution Approach 2:
The patent extracts only the most relevant features from dense document information using rule-based methods and simple ML models, rather than processing the entire document with complex deep learning models. This extraction approach reduces computational load while preserving prediction accuracy.
2Reliability
If deep learning models are trained with voluminous diverse training data, then model performance is improved, but data requirements and training complexity increase
Solution Approach 1:
The patent changes the parameters of the learning problem by transforming unstructured document data into structured features with predefined schemas. This transformation allows simpler models to learn from smaller, more targeted training datasets while achieving comparable performance to deep learning models trained on voluminous data.
3Productivity
If feature extraction processing pipeline is implemented to reduce data dimensionality, then computational efficiency is improved, but model complexity increases
Solution Approach 1:
The patent performs preliminary feature extraction and data transformation before the main classification task. By pre-processing documents to extract structured features (entities, relationships, key values), the system reduces the complexity of the subsequent classification problem, allowing simpler models to achieve high efficiency.
Data Source
AI summary
Aspects of the present disclosure are directed to resolving a latent status of document(s) that contain dense information using machine learning. Documents can be organized as a set of informational pages that together convey data associated with an organizational concept. Resolving a latent status from the pages of documents comprising dense information can be challenging, for example when indicator(s) of this latent status are hidden within the dense information. Implementations predict the latent status of document(s) via machine learning by performing a feature extraction processing pipeline. For example, a feature extraction processing pipeline can reduce the dimensionality of the document data, thus providing the machine learning model(s) a simplified problem. The machine learning model(s) can process the extracted data and, given the simplified problem, accurately predict the latent status of the document(s). In some implementations, the machine learning model(s) are trained using real-world training instances and synthetic training instances.