Semantic Document Layout Mapping for Flexible Template Generation
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Solution Overview
Problem
Existing document creation systems lack flexibility, accuracy, and operational efficiency when generating context-specific textual content using varied templates and diverse source documents, often requiring multiple device interactions and failing to maintain visual harmony due to rigid text mapping processes.
Innovation Solution
A document transformation system utilizing language machine learning models with semantic hierarchical transformations, including text classification and named entity recognition, to automatically extract key information from source documents and map it to target documents, adjusting content to match the target document's design constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If predefined textual content mapping is used, then document generation is simplified, but flexibility and accuracy deteriorate
Solution Approach 1:
The patent replaces traditional mechanical text mapping systems with an AI-based semantic understanding system. The AI model analyzes the meaning and context of source text, automatically determines appropriate target positions, and performs intelligent mapping without relying on predefined rigid templates, thereby achieving both simplicity and flexibility
Solution Approach 2:
The system changes the mapping parameters from fixed positional rules to dynamic semantic parameters. By using AI to understand text meaning, context, and relationships, the system adapts mapping behavior based on content characteristics rather than following predetermined patterns, improving both accuracy and flexibility
2Productivity
If rigid text mapping processes are used, then operational efficiency is improved, but visual harmony deteriorates
Solution Approach 1:
The patent transforms the static rigid mapping process into a dynamic adaptive process. The AI model continuously adjusts mapping decisions based on real-time analysis of text semantics, document structure, and visual requirements, enabling the system to maintain visual harmony while operating efficiently without predefined constraints
3Measurement precision
If multiple device interactions are required, then mapping accuracy is improved, but operational efficiency deteriorates
Solution Approach 1:
The patent implements a self-service mapping system where the AI model autonomously performs text analysis, position determination, and content mapping without requiring multiple user interactions. The system independently handles the complete mapping process, achieving both high accuracy through intelligent analysis and high efficiency by eliminating manual intervention steps
Data Source
AI summary
The present disclosure is directed toward systems, methods, and non-transitory computer readable media that transform textual content from a source document into a target document. In particular, the disclosed systems utilize a text classification model based on textual elements and corresponding text classes from the target document to map the textual content into hierarchical sections within a document hierarchy. Additionally, the disclosed systems utilize a natural language intent classification model to generate named entity classifications for the textual elements of the target document. The disclosed systems utilize source text of the source document, the named entity classifications, the text classes, and the document hierarchy to generate a text transformation prompt for a language machine learning model. The disclosed systems utilize the language machine learning model to map text from the source document to the target document.