Automated Document Highlighting via Machine Learning Graph Analysis
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Solution Overview
Problem
Conventional online document management systems lack the ability to perform intelligent analysis of document content for automatic highlighting of relevant passages, making it inefficient for users to find and understand important information within documents.
Innovation Solution
A method using a machine learned model trained on a set of pre-highlighted documents to identify and highlight relevant passages within a target document by converting text into word vectors, constructing an undirected weighted graph, and performing random walk iterations to rank text passages by relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional online document management systems are used to manually view and execute documents, then users can access documents, but users cannot efficiently locate relevant passages within documents
Solution Approach 1:
The patent replaces manual document review with an automated machine learning-based highlighting system. The system converts document text into word vectors, constructs graphs representing document structure, and uses random walk algorithms to automatically identify and highlight relevant passages, eliminating the need for users to manually search through documents.
Solution Approach 2:
The document management system performs self-service by automatically analyzing document content and highlighting relevant passages without user intervention. The machine learning model autonomously processes documents, identifies important sections based on learned patterns from training data, and presents highlighted results to users, making the system self-sufficient in improving document navigation.
2Extent of automation
If no intelligent analysis is provided for document content, then the system remains simple, but relevant passages cannot be automatically highlighted
Solution Approach 1:
The patent segments the document analysis process into distinct computational stages: text preprocessing and word vector conversion, graph construction from document structure, random walk algorithm execution, and result aggregation. This segmentation allows the complex task of intelligent highlighting to be broken down into manageable, modular components that can be implemented and maintained separately.
Solution Approach 2:
The patent introduces intermediate data structures to bridge simple text input and complex analysis output. Word vectors serve as an intermediary representation converting text to numerical form, graphs serve as intermediaries modeling document structure and relationships, and these intermediaries enable the machine learning model to process and analyze document content effectively without requiring direct complex analysis of raw text.
3Adaptability or versatility
If pre-programmed rules are used for document highlighting, then the system is easy to implement, but it cannot adapt to various document types
Solution Approach 1:
The patent employs parameter changes by training the machine learning model on diverse training documents with varying characteristics. The model learns to adjust its analysis parameters and highlighting criteria based on the specific document type, content, and structure it encounters, enabling it to adapt automatically to different document formats without requiring manual reprogramming for each document type.
Solution Approach 2:
The patent performs preliminary action by pre-training the machine learning model on a comprehensive dataset of training documents before deployment. This preliminary training phase allows the system to learn document patterns, relationships, and highlighting criteria in advance, so that when the system processes actual documents, it can immediately apply learned knowledge to adaptively highlight relevant passages across various document types without requiring real-time rule configuration.
Data Source
AI summary
A highlighting engine modifies a target document by identifying and highlighting a set of text passages. The highlighting engine receives a training set of data including documents that each include a set of highlighted text passages. The highlighting engine trains a machine learned model using the training set of data. The trained machine learned model, when applied to subsequent identified candidate sets of text passages within the target document, identifies the set of text passages to highlight. The highlighting engine modifies the target document with the highlighted set of text passages and provides the modified target document for display via an interface. The highlighted set of text passages enable a user to quickly read and understand the target document.