ML Framework for Text Document Segmentation and Template Ranking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in transforming overloaded text documents into visually appealing segments with seamless transitions, as existing methods require significant time and effort, and many users are unaware of or unable to utilize advanced features for creating engaging presentations.
Innovation Solution
A machine learning-powered framework that automatically generates visually appealing segments and smooth morph transitions by analyzing text documents and leveraging user preferences, using offline and online document systems to rank candidate templates and apply aesthetic assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually break up text into multiple segments and add seamless transitions with suitable backgrounds, then the presentation becomes more engaging and easier to understand, but the process becomes time-consuming and requires professional art design skills
Solution Approach 1:
The system enables automatic self-service by using machine learning models to autonomously segment text documents, select appropriate templates with backgrounds, and apply morph transitions without requiring user intervention or professional design skills. The framework automatically analyzes the text content and generates visually appealing segmented presentations.
Solution Approach 2:
The patent replaces the manual mechanical process of text segmentation and transition design with an automated machine learning-based system. The ML models substitute for human designers by automatically performing text analysis, template selection, and visual effect application, eliminating the need for professional art design knowledge.
2Manufacturing precision
If users utilize advanced features for creating video-like effects and smooth transitions, then the presentation quality improves, but users need professional art design knowledge and strategies to fully utilize these features
Solution Approach 1:
The system performs self-service by automatically selecting and applying appropriate templates, backgrounds, and morph transitions based on the analyzed text content. The machine learning models autonomously make design decisions that previously required professional art design knowledge, making advanced features accessible to ordinary users.
Solution Approach 2:
The framework changes the parameters of template selection and transition application by using machine learning models to automatically adjust and optimize design parameters based on text analysis results. This transforms the complex manual parameter adjustment process into an automated system that adapts to different content types.
3Loss of information
If text is added extensively into slides or word documents, then the message delivery capacity increases, but the document becomes overloaded and harder to understand and remember
Solution Approach 1:
The system applies segmentation by automatically dividing overloaded text documents into multiple smaller, manageable segments or slides. The machine learning model analyzes the text structure and content to create logical divisions that maintain complete message delivery while improving readability and retention through appropriate chunking.
Solution Approach 2:
The framework applies local quality by assigning different visual characteristics, templates, and backgrounds to different segments based on their specific content requirements. Each segment receives customized visual treatment that enhances its specific message, making the overall presentation more engaging and easier to understand.
Data Source
AI summary
Systems and methods for providing a machine learning-powered framework to transform overloaded text documents is provided. The system generates a plurality of candidate templates offline. During runtime, the system accesses a text document and analyzes the text document to identify segmentation data. The segmentation data can indicate a plurality of segments derived from the text document. The system then accesses a plurality of candidate templates, whereby each candidate template comprises a plurality of pages having a different background element that shares a common theme. The plurality of candidate templates are ranked based on at least the segmentation data. The network then generates multiple presentation pages for each of a predetermined number of top ranked candidate templates by incorporating each of the plurality of segments into a corresponding page of the plurality of pages for each of the top ranked candidate templates. The multiple presentation pages are presented for each of the top ranked candidate templates as a recommendation.