Ink Stroke Formatting Using Semantic Context and Letter Spacing
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Solution Overview
Problem
Conventional inking applications on computing devices have limited functionality compared to text-editing applications, leading to inefficiencies and decreased productivity due to the inability to accurately predict ink strokes and adjust spacing based on user penmanship and semantic context.
Innovation Solution
A system utilizing trained machine-learning models to generate predicted ink strokes by processing ink stroke data with ink-based and text-based semantics, incorporating user-specific training and semantic context to improve accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional inking applications are used, then users can provide ink stroke information to the computing device, but the functionality is limited and editing is frustrating compared to text-editing applications
Solution Approach 1:
The patent introduces an intermediary system comprising machine learning models that translate between ink stroke data and text data. The first model converts ink strokes to text, while the second model generates predicted ink strokes from text, enabling bidirectional translation that bridges the functionality gap between inking and text-editing applications.
Solution Approach 2:
The patent replaces manual editing operations with automated machine learning-based prediction and generation systems. Instead of requiring users to manually edit ink strokes, the system uses trained models to automatically generate, predict, and convert ink stroke data, substituting mechanical user actions with intelligent automated processes.
2Measurement precision
If ink stroke data is processed without semantic context, then processing is simpler, but prediction accuracy and user-specific customization are reduced
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models with user-specific ink writing samples and semantic context data before actual ink stroke processing. This preliminary training phase enables the models to understand user handwriting patterns and semantic relationships, improving prediction accuracy without adding complexity to the real-time processing workflow.
Solution Approach 2:
The patent combines multiple types of data (ink stroke data, text data, semantic context) and multiple model outputs into a composite processing system. The first model processes ink strokes to generate text, while the second model integrates this text with semantic context to generate predicted ink strokes, creating a composite intelligence system that leverages multiple data sources.
3Productivity
If multiple machine-learning models are used to generate predicted ink strokes, then prediction accuracy and customization improve, but system complexity increases
Solution Approach 1:
The patent segments the ink stroke processing task into two distinct functional modules: the first model for converting ink strokes to text, and the second model for generating predicted ink strokes from text and semantic context. This segmentation allows each model to specialize in its specific function, improving overall productivity while managing system complexity through modular design.
Data Source
AI summary
In some examples, systems and methods for formatting ink are provided. Ink stroke data may be received, letters may be identified from the ink stroke data, and spacing may be identified between the letters. A user command and semantic context may be received. An action may be determined based on the user command, the semantic context, and the spacing between the letters. Further, the action may be performed.