Text Record-Based Format Prediction for Documents
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Solution Overview
Problem
Electronic document processing applications face inefficiencies in formatting electronic documents, including high network bandwidth and processing resource consumption due to manual formatting changes, limited and cumbersome formatting rules, and inaccurate formatting predictions.
Innovation Solution
Identifying a text region in an electronic document, determining a candidate text portion based on predetermined patterns, matching it with stored text records, and confirming the appropriate formatting type through individual word matches to provide accurate formatting suggestions to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual formatting changes are performed in electronic document processing, then formatting flexibility is improved, but network bandwidth and processing resource consumption increase
Solution Approach 1:
The system performs self-service by automatically analyzing text regions, comparing them against stored patterns, and applying formatting suggestions without requiring manual user intervention for each formatting decision, thereby reducing both network bandwidth and processing resource consumption while maintaining formatting flexibility
Solution Approach 2:
The system performs preliminary action by pre-storing text patterns and formatting rules in a database before actual document processing occurs. When processing documents, the system queries these pre-stored patterns rather than generating formatting rules in real-time, significantly reducing processing resource consumption during active document editing
2Adaptability or versatility
If formatting rules are made comprehensive to cover various document types, then formatting coverage is improved, but device complexity increases
Solution Approach 1:
The system segments formatting rules into distinct, modular patterns stored in a database. Each pattern represents a specific formatting scenario (e.g., headings, paragraphs, lists) that can be independently stored, retrieved, and applied. This segmentation allows comprehensive formatting coverage while keeping individual rule complexity manageable and the overall system structure organized
Solution Approach 2:
The system introduces an intermediary pattern-matching layer between the user's text input and the formatting application. Instead of directly implementing complex formatting logic, the system mediates through stored patterns that act as intermediaries, matching text regions against predefined patterns and automatically applying appropriate formatting based on the best match
3Productivity
If formatting predictions are made quickly to improve editing speed, then productivity is improved, but formatting accuracy decreases
Solution Approach 1:
The system replaces mechanical pattern-matching methods with machine learning-based prediction algorithms. The machine learning model analyzes text regions and predicts appropriate formatting by learning from trained patterns, providing both quick predictions and high accuracy simultaneously. This substitution enables the system to maintain productivity through fast predictions while improving formatting accuracy through intelligent pattern recognition
Data Source
AI summary
A method for training a machine learning model included generating training data for the machine learning model. Generating the training data includes generating first training input that includes candidate text portions of one or more electronic documents and generating a first target output for the first training input. The first target output identifies a formatting type for each of the candidate text portions. The training data is provided to train the machine learning model on (i) a set of training inputs including the first training input, and (ii) a set of target outputs including the first target output.