Domain-Aware Document Classification and Information Extraction
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Solution Overview
Problem
The indirect lending industry faces challenges in efficiently and accurately classifying and extracting information from consumer documents, leading to time-consuming and error-prone processes.
Innovation Solution
A computer-implemented system and method for domain-aware document classification and information extraction, utilizing a combination of state-of-the-art classifiers and domain knowledge-inspired engines to automate the classification and extraction of relevant information from consumer documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual document classification and information extraction is performed by funders, then accuracy can be maintained through human judgment, but time consumption increases significantly and error rates rise due to repetitive manual processing
Solution Approach 1:
The patent replaces the mechanical manual processing system with an automated computer vision and machine learning system. The system uses document classification models, information extraction algorithms, and neural networks to automatically classify documents and extract relevant information, substituting human manual operations with automated computational processes that achieve both high accuracy and efficiency
Solution Approach 2:
The system enables documents to be processed autonomously without human intervention. The automated classification and information extraction system performs self-service by independently analyzing documents, identifying their types, and extracting required information fields, eliminating the need for manual funder review while maintaining consistent accuracy
2Adaptability or versatility
If multiple document types with varying formats and qualities are processed, then the system must be highly adaptable, but the complexity of processing increases and error rates rise
Solution Approach 1:
The patent implements a universal document processing system that can handle multiple document types (pay stubs, bank statements, tax returns, employment verification) through a single integrated platform. The system uses multi-functional algorithms that adapt to different document formats and qualities, providing consistent processing capabilities across diverse document types without requiring separate specialized systems
Solution Approach 2:
The system dynamically adjusts processing parameters based on document characteristics. The machine learning models adapt their classification thresholds, extraction fields, and validation rules according to the specific document type, format, and quality detected, allowing the system to handle varying document parameters efficiently while maintaining processing consistency
3Productivity
If conventional document processing systems are used, then implementation is straightforward, but they cannot meet the need for timely and accurate document classification and information extraction
Solution Approach 1:
The system performs preliminary document classification and information extraction before final verification. The automated system pre-processes documents by identifying their types and extracting key information fields in advance, preparing the data for subsequent validation and review steps, thereby improving overall processing efficiency and accuracy through advance preparation
Solution Approach 2:
The system implements feedback mechanisms where classification and extraction results are continuously validated and refined. The machine learning models learn from processing outcomes and adjust their parameters based on feedback from verification steps, improving both productivity and reliability through iterative optimization and continuous learning
Data Source
AI summary
A system and method for domain aware document classification and information extraction from consumer documents are disclosed. A particular embodiment is configured to: establish, by use of a data processor and a data network, a data connection with at least one applicant platform; receive an upload of documents from the applicant platform via the data network; classify each document as being of a particular document type; determine an information extraction strategy based on a document type classification of a particular document; and extract information from the particular document based on the information extraction strategy.