Dialog Tree Generation from Document Visual Structure
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Solution Overview
Problem
Conventional dialog systems require significant time, cost, and effort for manual development by human operators, necessitating an automated and efficient method for generating dialog trees.
Innovation Solution
An automated dialog tree generation system that includes a processor and memory to parse documents, extract visual design elements, generate a content structure, and create a dialog decision tree with a hierarchy of nodes, minimizing human editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If human operators manually develop dialog systems, then the dialog systems can be created with high precision, but significant time, cost, and effort are required
Solution Approach 1:
The patent replaces the manual mechanical process of human operators creating dialog trees with an automated computer-based system. The system uses processors to automatically parse documents, extract visual design elements, generate content structures, and create dialog decision trees, eliminating the need for manual human labor while maintaining high precision through automated algorithms and machine learning models.
Solution Approach 2:
The dialog system generation process becomes self-service through automation. The system automatically processes input documents, extracts necessary information, generates content structures, and creates dialog trees without requiring continuous human intervention. The automated pipeline enables the system to serve itself in generating dialog structures from provided documents.
2Manufacturing precision
If human operators manually develop dialog systems, then the dialog systems can be created with high precision, but significant cost and effort are required
Solution Approach 1:
The patent replaces the manual mechanical process of human operators creating dialog trees with an automated computer-based system. The system uses processors to automatically parse documents, extract visual design elements, generate content structures, and create dialog decision trees, eliminating the need for manual human labor while maintaining high precision through automated algorithms and machine learning models.
3Productivity
If automated methods are used to generate dialog trees, then time and cost efficiency are improved, but the complexity of the generation system increases
Solution Approach 1:
The automated dialog generation system is divided into distinct modular components: a document parser module that parses input documents into raw blocks, a visual design element extractor that identifies design elements from raw blocks, a content structure generator that creates content structures from visual elements, and a dialog tree generator that produces dialog decision trees from content structures. This segmentation allows each module to perform a specific function independently, managing overall system complexity while maintaining high productivity.
4Speed
If automated document parsing is used, then processing speed is improved, but the precision of extracting visual design elements may be affected
Solution Approach 1:
The patent employs automated computer-based processing to replace manual analysis, using algorithms and machine learning models to accurately identify and extract visual design elements from parsed document blocks. The system maintains precision through trained models that can recognize patterns and characteristics of design elements, achieving both high speed processing and accurate extraction simultaneously.
Data Source
AI summary
A dialog tree generation system is provided, including a processor, and a memory storing instructions that, when executed by the processor, cause the system to receive documents, parse the documents into raw blocks, extract visual design elements from the raw blocks, generate a content structure from the extracted visual design elements, generate at least a dialog decision tree based on the extracted content structure, the dialog decision tree comprising a plurality of nodes organized into a hierarchy, and output the dialog decision tree.