Document Element Layout Adjustment via Machine Learning
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Solution Overview
Problem
Existing technologies lack efficient automated methods for adjusting the layout of document elements within a resizable region of a document when the aspect ratio is changed, leading to burdensome manual adjustments.
Innovation Solution
A computer-implemented method that accesses region data defining a document region, including dimension and layout characteristics of document elements, and region modification data to resize the region. The method encodes this data and inputs it into a trained machine learning model to determine modified layout characteristics for the document elements, resulting in an adjusted layout.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual layout adjustment is used to maintain document quality after aspect ratio change, then document comprehension and information conveyance are preserved, but designer workload and time consumption increase significantly
Solution Approach 1:
The system enables self-service automated layout adjustment where the computer system automatically modifies document element positions and sizes based on aspect ratio changes without requiring designer intervention. The system encodes dimension data, element data, and region modification data, processes it through a machine learning model to generate modified element data, and applies the adjusted layout automatically.
2Productivity
If automated layout adjustment methods are implemented, then designer workload is reduced, but the ability to maintain clear information conveyance and meaningful document layout deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model processes encoded document data and region modification data to generate modified element data that maintains layout quality. The model learns from training data to predict appropriate layout adjustments that preserve information conveyance and document meaning while adapting to aspect ratio changes.
Solution Approach 2:
The system changes parameters of document elements (positions, sizes, orientations) based on the encoded dimension data and region modification data. The machine learning model determines optimal parameter modifications to maintain layout quality while adapting to the new aspect ratio, ensuring information remains clearly conveyed.
3Manufacturing precision
If comprehensive manual review of all document elements is performed to achieve desired layout, then layout quality is maintained, but resource consumption and complexity increase unduly
Solution Approach 1:
The system replaces the mechanical manual review and adjustment process with an automated computational system. The computer system encodes document data and region modification data, processes it through a machine learning model to generate modified element data, and automatically applies the adjusted layout, eliminating the need for manual review of all document elements while maintaining layout precision.
Data Source
AI summary
Embodiments of a computer implemented method for document element layout adjustment, are described. In some embodiments dimension data, elements data and region modification data are encoded into encoding data, which is input into a trained machine learning model, which determines modified elements data defining a modification to layout characteristics. In some embodiments dimension data, elements data and region modification data are encoded into encoding data, which is then modified based on excluded elements data, prior to input into a trained machine learning model for determining modified elements data defining a modification to layout characteristics.