Machine Learning Pre-fill Engine for Document Data Extraction
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Solution Overview
Problem
Current automated techniques for extracting data from documents are not designed to analyze and extract specific fields related to business processes, such as insurance, and require continuous human effort and maintenance, leading to inefficiencies and potential biases.
Innovation Solution
A system utilizing a machine learning algorithm with a pre-fill engine that receives electronic documents, seed dataset documents, and pre-fill questions to determine output data and questions relevant to a particular field of analysis, using terminology, categories, and ontology, and presenting answers through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised machine learning techniques are used to extract data from documents, then extraction accuracy can be improved, but continuous human effort and maintenance are required, increasing cost and time consumption
Solution Approach 1:
The system performs preliminary actions by automatically creating seed datasets and training machine learning models in advance. The pre-fill engine generates initial data extracts and questions before human review, so that when documents are processed, the system already has structured frameworks and examples to guide accurate extraction without requiring continuous manual retraining
Solution Approach 2:
The system enables self-service by allowing the machine learning model to autonomously create seed datasets, generate pre-fill questions, and perform data extraction without continuous human intervention. The model uses its own outputs to refine future extractions, creating a self-improving system that reduces dependency on ongoing manual maintenance while maintaining high accuracy
2Productivity
If existing automated techniques are used for data extraction, then processing speed can be improved, but the techniques are not designed to analyze specific fields related to business processes, leading to incorrect extraction
Solution Approach 1:
The system applies local quality by customizing the machine learning model to understand and extract specific fields relevant to particular business processes. Instead of using generic extraction techniques, the system trains domain-specific models that learn the nuances of insurance terminology, document structures, and field relationships, enabling both high speed and high accuracy for field-specific extraction
Solution Approach 2:
The system introduces an intermediary layer in the form of a pre-fill engine that acts as a mediator between the raw documents and the final extracted data. This intermediary component translates domain-specific business process requirements into machine-understandable queries and validation rules, enabling the system to maintain both speed and precision by filtering and guiding the extraction process through structured intermediaries
3Measurement precision
If human effort is used to create seed datasets for machine learning, then extraction accuracy can be improved, but the process is expensive and error-prone
Solution Approach 1:
The system implements self-service by enabling the machine learning model to autonomously create seed datasets without human intervention. The pre-fill engine automatically generates training data by processing documents and creating structured examples that the model can learn from, eliminating the expensive and error-prone manual annotation process while maintaining high extraction accuracy through automated data synthesis
Solution Approach 2:
The system uses copying by having the pre-fill engine create synthetic seed datasets that replicate the structure and content patterns of real business documents. These copied datasets serve as training material for the machine learning model, providing accurate extraction examples without requiring humans to manually create and verify each training sample, thus reducing costs and errors
Data Source
AI summary
Computer implemented systems and methods are disclosed for extracting specific data using machine learning algorithms. In accordance with some embodiments, a memory device that stores at least a set of computer executable instructions for a machine learning algorithm and a pre-fill engine; and at least one processor that executes the instructions that cause the pre-fill engine to perform functions that include: receiving electronic documents, seed dataset documents, and pre-fill questions; determining output data that enable navigation through the electronic documents using the machine learning algorithm; determining output questions that enable navigation through the electronic documents using the machine learning algorithm; determining output documents to enable navigation through the electronic documents using the machine learning algorithm; and presenting one or more answers for one or more of the output questions using a graphical user interface.