Document Field Identification Using Weighted Positional Context
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Solution Overview
Problem
Current systems for identifying relevant content in documents face challenges such as template-based approaches failing with format changes, label-based methods producing false positives, and transformer models lacking transparency and scalability, especially with sensitive data handling and computational costs.
Innovation Solution
A system and method using a context window identification model and a field element identification model to identify content of interest by analyzing positional data within a context window, involving training on documents to determine frequency, eliminating irrelevant elements, and adjusting parameters for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If template-based approach is used to identify content, then extraction is effective for standardized documents, but it fails when documents have format changes or variations
Solution Approach 1:
The patent transitions from fixed template parameters to adaptive parameter estimation. The system estimates parameters such as text orientation, line spacing, and element positioning dynamically from the document content itself, allowing the same extraction mechanism to adapt to various formats without requiring multiple predefined templates.
Solution Approach 2:
The system performs self-calibration by automatically analyzing the document structure and adjusting its own extraction parameters based on the actual content layout. This self-service capability eliminates the need for manual template configuration and enables the system to handle format variations autonomously.
2Ease of operation
If label-based heuristic approach is used, then content can be searched by labels, but it produces false positives and requires aggregation of multiple labels
Solution Approach 1:
The patent replaces the mechanical label-matching process with a machine learning-based classification system. Instead of relying on predefined labels and fuzzy text searches, the system uses trained neural networks to directly identify and extract content elements, eliminating false positives associated with label-based approaches.
3Measurement precision
If transformer models are used for content detection, then sophisticated content detection is achieved, but computational costs and data privacy concerns arise
Solution Approach 1:
The patent segments the content extraction task into multiple independent components, each handled by specialized models. Instead of using a single large transformer model to process entire documents, the system divides the document into sections and uses targeted models for specific extraction tasks, reducing overall computational requirements while maintaining accuracy.
4Adaptability or versatility
If multiple templates are created to handle template variations, then format flexibility is improved, but the number of representational templates increases making the system complex
Solution Approach 1:
The patent creates a single universal extraction mechanism that can handle multiple document formats through parameter estimation rather than requiring separate templates for each format. This universal approach eliminates the complexity of maintaining multiple specialized templates while preserving the ability to extract content from various document types.
Data Source
AI summary
The present invention discloses a system and method for identifying a content of interest in a document. The method includes receiving a plurality of training documents that comprise a plurality of field elements, estimating for each data element present within the training document, a weighted distance of the each data element from a field element that corresponds to the content of interest. A feature vector is created based on the weighted distance and a position of the each data element with respect to the field element. A set of feature vectors are developed for the plurality of field elements and is used for training a field identification model. The field identification model is applied on the document to identify a beginning position of the content of interest.