Automated Field Indexing in Digital Records via ML Segmentation
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Solution Overview
Problem
Current technologies struggle to automate the indexing and extraction of targeted and complex information from digital data records, particularly in documents like Safety Data Sheets (SDS) that contain highly variable and structured information.
Innovation Solution
The system employs a combination of machine learning techniques, including Natural Language Processing (NLP) and Computer Vision (CV), along with rule-based systems, to identify, index, and extract specific information fields from digital data records. This involves training NLP models on specific target fields, using pre-processed text to determine candidate texts, and extracting relevant information using trained ML models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based systems are used to extract information from digital data records, then the system is simple to implement, but it cannot handle highly variable and structured information effectively
Solution Approach 1:
The system segments the information extraction task into multiple specialized machine learning models, each trained on specific target fields (e.g., chemical ingredients, hazard statements). This segmentation allows each model to focus on particular patterns while collectively handling the full variability of SDS documents, resolving the contradiction between simple rule-based implementation and adaptability to variable structures.
Solution Approach 2:
The system changes the parameters of the extraction approach by transitioning from fixed rule-based patterns to machine learning models with adjustable parameters that can adapt to different document structures. The ML models learn optimal parameters for identifying and extracting information from highly variable SDS formats, maintaining both implementability and adaptability.
2Measurement precision
If multiple machine learning models are trained on specific target fields, then the extraction precision is improved, but the system complexity increases
Solution Approach 1:
The system divides the complex extraction task into multiple specialized ML models, each handling specific target fields. This segmentation improves precision for each field while managing overall system complexity through modular architecture, where each model can be independently trained and maintained.
Solution Approach 2:
The system creates a universal framework that can handle multiple different target fields using the same underlying ML architecture. This multi-functionality approach allows the system to maintain high precision across various fields without proportionally increasing complexity, as the same framework serves multiple purposes.
3Productivity
If automated indexing is implemented for complex data structures, then productivity is improved, but the difficulty of detecting and measuring information increases
Solution Approach 1:
The system transforms the detection approach by using ML models that can automatically adapt to varying information structures in complex data records. This parameter-based learning enables high-speed automated indexing while handling the inherent difficulty of detecting and measuring information in unstructured or semi-structured formats.
Solution Approach 2:
The ML models perform self-learning and self-adjustment to handle the complexity of information detection in complex data structures. This self-service capability allows the system to maintain high productivity without requiring manual intervention to manage the difficulty of detecting varied information patterns.
Data Source
AI summary
Systems and Methods are disclosed herein for automatically indexing multiple informational fields in digital data records, the method comprising: identifying, based on rules defining target information fields, for each target field of the target information fields, at least one page in a digital data record comprising content related to the target field; extracting, for each target field, from the identified at least one page, at least one portion of text comprising the content; feeding, for each target field, a pre-processed version of the at least one portion of text into a machine learning (ML) model, wherein the ML model is trained on the target field; determining, for each target field, via the ML model trained on the target field, at least one candidate text comprising the content; and extracting, for each target field, the at least one candidate text.