Document Package Splitting With ML Topic Labeling
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Solution Overview
Problem
Existing electronic document systems struggle with efficiently splitting and labeling large document files containing multiple documents, leading to manual effort, errors, and computational inefficiencies, especially when handling mega-sized files.
Innovation Solution
An electronic document system employs an ensemble machine learning model to identify and split component documents within a document package, utilizing two separate models for document identification and topic recognition, enhancing navigability and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual splitting and review of document files is performed, then document accuracy can be maintained, but user time and effort increase significantly
Solution Approach 1:
An ensemble machine learning model acts as an intermediary between the document file and the user, automatically performing the splitting and labeling tasks. The model includes a document splitter that identifies page breaks and a document labeler that assigns topics, eliminating the need for manual review while maintaining high accuracy through automated intelligent analysis
Solution Approach 2:
The manual mechanical process of reviewing and splitting documents is replaced with an automated machine learning system. The ensemble model uses trained algorithms to detect document boundaries and classify content, substituting human cognitive effort with computational processing that is both faster and equally accurate
2Loss of time
If existing automatic splitting systems are used, then user time is reduced, but they cannot handle large mega-sized document files efficiently
Solution Approach 1:
The ensemble machine learning model is segmented into specialized components: a document splitter that handles page boundary detection and a document labeler that handles topic classification. This modular architecture allows each component to be optimized independently, enabling efficient processing of large mega-sized files while maintaining the ability to handle diverse document types and sizes
Solution Approach 2:
The system changes the processing parameters by using trained machine learning models that can dynamically adjust to different document formats, sizes, and complexities. The model processes documents based on learned patterns rather than fixed rules, allowing it to efficiently handle files of varying magnitudes from small to mega-sized without performance degradation
3Loss of information
If users review each page to understand document topics, then document understanding improves, but time consumption and error rate increase
Solution Approach 1:
The document labeling system performs self-service by automatically analyzing document content and assigning appropriate topic labels without user intervention. The machine learning model independently processes each document, extracts key features, and generates topic classifications, providing users with pre-labeled documents that require minimal verification
Solution Approach 2:
The system performs preliminary action by pre-labeling documents with their topics before user review. The document labeler processes and categorizes content in advance, so when users access the documents, the topic understanding is already established, eliminating the need for users to perform the time-consuming task of reading and comprehending each document to understand its topic
Data Source
AI summary
An electronic document system can allow users to upload a document package containing multiple individual component documents. Each component document includes a subset of a plurality of pages that are included in the document package. The electronic document system identifies a page of each component document by applying a machine learning model to the document package. The electronic document system partitions the document package into the individual component documents based on the identified pages. For each individual component document, the electronic document system identifies a document topic corresponding to the component document by applying another machine learning model. The electronic document system modifies a user interface to display each component document and corresponding document topic.