Smart Feature Suggestions for Document Beautification
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Solution Overview
Problem
The process of document beautification is time-consuming due to the effort required to consistently apply design attributes across various sections of a document, as designers often need to manually explore and apply attributes such as font selection and sizes, which can be tedious and inefficient.
Innovation Solution
A self-learning model and apparatus for smart feature suggestions based on textual analysis that automatically classifies sections of a document, ranks applicable attributes, and presents them to users in an ordered list, allowing for easy selection and application, while also learning from user preferences to improve future suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual exploration and application of design attributes is performed, then design quality and consistency can be achieved, but time consumption and editorial effort increase significantly
Solution Approach 1:
The system performs self-learning by automatically analyzing user attribute selections and document structures, then generates intelligent suggestions for applying attributes to similar sections. This self-service mechanism reduces manual exploration time while maintaining design consistency through learned patterns from user preferences and document context.
Solution Approach 2:
The system incorporates feedback loops where user selections of attributes are continuously analyzed and used to improve future suggestions. The machine learning model learns from each interaction, adjusting its recommendations to better match user preferences and document requirements, thereby reducing time while preserving quality.
2Manufacturing precision
If consistent application of design attributes across all sections is performed, then document quality is improved, but editorial effort and complexity increase
Solution Approach 1:
The system introduces an intermediary intelligent suggestion layer between the user and the attribute application process. This intermediary analyzes document structures, identifies similar sections, and provides curated attribute recommendations, simplifying the complexity while ensuring consistent application across sections through learned best practices.
Solution Approach 2:
The system performs preliminary analysis of document structures and user preferences before attribute application is needed. By pre-processing the document to identify sections and their relationships, and pre-learning user attribute selections, the system reduces the complexity of consistent attribute application during the actual beautification process.
3Manufacturing precision
If manual exploration of design attributes is performed, then customized design quality is achieved, but productivity decreases
Solution Approach 1:
The system enables self-service through automatic suggestion generation that learns from user preferences and document context. This allows rapid attribute application while maintaining customized design quality, as the system adapts to user needs without requiring manual exploration for each section.
Solution Approach 2:
The system replaces the mechanical manual process of exploring and applying attributes with an intelligent automated suggestion system. This substitution maintains design quality through learned patterns while dramatically increasing productivity by eliminating repetitive manual exploration across document sections.
Data Source
AI summary
Techniques are provided for a computer processor-implemented method of beautifying an electronic textual document having text organized in a plurality of sections. Each of the sections is representative of a corresponding one of a plurality of textual elements. The method includes: receiving a user selection of the text for beautification, the selected text including at least a portion of one of the sections; classifying the selected text as the corresponding one of the textual elements represented by the one of the sections; ranking a set of attributes applicable to the one of the textual elements; presenting an ordered list of the highest-ranked attributes; in response to receiving a selection of one or more of the presented attributes, applying the selected attributes to the selected text; and reranking the applicable attributes to reflect the selected attributes.