Document Classification via Signatory Role Analysis
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Solution Overview
Problem
Current systems fail to accurately classify digital documents in multi-document transactions based on signatory roles, leading to inefficiencies in document management and review processes, particularly in financial, insurance, and legal transactions, where documents lack textual labels or metadata identifying logical types.
Innovation Solution
A computer system converts scanned document images into text, analyzes signature elements and context to predict signatory roles, and classifies documents into logical types based on expected signatory roles, using a trained classification model to assign categories and reduce human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual review of scanned document images is used, then document classification accuracy can be maintained, but productivity is reduced and loss of time increases
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated computer vision system that uses optical character recognition (OCR) and machine learning algorithms to extract and analyze signature elements, document types, and contextual information, thereby substituting human labor with automated technological systems to improve productivity
Solution Approach 2:
The system enables documents to be automatically classified without human intervention by implementing self-service classification through trained machine learning models that independently analyze document features, extract signature information, and categorize documents based on learned patterns from training data
2Productivity
If automated classification without signatory role analysis is used, then productivity increases, but measurement precision of document categorization deteriorates
Solution Approach 1:
The system performs preliminary training with labeled training data containing annotated signature elements and document characteristics before deployment. This preliminary action of training the machine learning models with ground truth data establishes accurate classification criteria that enable precise document categorization during automated operation
Solution Approach 2:
The system incorporates feedback mechanisms where classification results are continuously refined based on performance metrics and can be retrained with additional labeled data. The feedback loop between classification outcomes and model improvement ensures maintaining high measurement precision while operating at automated speeds
3Measurement precision
If detailed signatory role analysis is performed, then measurement precision of classification improves, but device complexity increases
Solution Approach 1:
The patent segments the document analysis process into distinct modular components: OCR text extraction, signature element detection, document type identification, and classification decision-making. Each module handles a specific aspect of analysis independently, reducing overall system complexity while maintaining high classification precision through coordinated operation of specialized sub-systems
4Measurement precision
If manual document classification is used, then measurement precision can be maintained, but productivity decreases and loss of time increases
Solution Approach 1:
The patent replaces manual classification mechanics with automated computer vision and machine learning systems that process documents at scale, maintaining measurement precision through trained algorithms while increasing productivity by eliminating the bottleneck of human review for large volumes of documents
Data Source
AI summary
A classifier receives a digital scan of a document and converts the content of the document in the digital scan from an image into text. The classifier analyzes the text to determine one or more predicted roles of one or more signatories, each predicted role determined based on one or more signature elements in the content of the document executed by the one or more signatories. The classifier evaluates each of the one or more predicted roles in view of a plurality of expected signatory role characteristics of a plurality of categories of documents of a transaction to select a particular category associated with the document from among the plurality of categories. The classifier classifies the document within the transaction as a particular logical type identified by the particular category from among a plurality of logical types for the transaction.