Handwriting Stroke Prediction Latency Reduction
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Solution Overview
Problem
Existing computing devices experience significant latency when processing and displaying handwriting input, leading to a disconcerting perception of unresponsiveness and affecting user experience, especially in applications requiring real-time feedback and accuracy.
Innovation Solution
A method implemented by a computing device to process handwriting by registering motion events, computing a confidence score based on curvature, speed, and acceleration, and dynamically controlling a prediction process to adaptively predict and display next points, thereby reducing latency and improving responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional prediction algorithms are used to reduce latency, then the system becomes more responsive, but prediction accuracy deteriorates causing artifacts and overshoots
Solution Approach 1:
The patent implements a dynamic prediction process that adapts the prediction window size (N2) based on computed confidence scores. The system transitions from static prediction parameters to dynamic adjustment, where the prediction behavior changes in real-time based on handwriting characteristics such as curvature, speed, and acceleration. This allows the system to maintain high responsiveness while preserving accuracy by adjusting prediction aggressiveness according to confidence levels.
Solution Approach 2:
The patent changes key parameters including the prediction window size (N2), confidence score thresholds, and curvature score ranges. By dynamically adjusting these parameters based on real-time analysis of handwriting motion characteristics, the system optimizes the balance between responsiveness and prediction accuracy, avoiding the fixed-parameter limitations of conventional approaches.
2Productivity
If prediction algorithms are applied to all handwriting inputs, then responsiveness improves, but errors and artifacts increase in certain handwriting styles
Solution Approach 1:
The patent applies prediction selectively based on local characteristics of the handwriting input. By computing confidence scores specific to each handwriting segment and applying prediction only where confidence thresholds are met, the system ensures high reliability in regions where prediction is appropriate while maintaining responsiveness overall. This local adaptation prevents universal application errors.
Solution Approach 2:
The system incorporates feedback loops where prediction outcomes and handwriting characteristics are continuously analyzed to adjust future prediction behavior. Confidence scores are computed based on real-time feedback from curvature, speed, and acceleration measurements, creating a closed-loop system that improves reliability by learning from and adapting to actual handwriting patterns.
3Device complexity
If a fixed prediction window size is used, then processing is simpler, but accuracy varies widely depending on handwriting style
Solution Approach 1:
The patent replaces fixed prediction window sizing with dynamic adjustment based on confidence scores derived from handwriting characteristics. The prediction window (N2) expands or contracts automatically according to the confidence level, allowing the system to maintain simplicity in implementation while achieving variable precision adapted to different handwriting styles and motion patterns.
Data Source
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AI summary
The invention relates to a method and a computing device (DV1) for processing handwriting (HW) comprising strokes (ST) of digital ink, each stroke comprising at least one point (PT). The method comprises: registering first points (PT1) as motion events; displaying, as a first stroke portion, second points (PT1a) comprising at least part of the first points (PT1); determining an integer N1 of the most recent first points (PT1c) registered over a predetermined time window; computing a confidence score (CF) based at least on a curvature score (SC1) representative of a curvature of the N1 most recent first points (PT1c); setting, based on the confidence score (CF), an integer N2 defining a prediction window; and controlling a dynamic prediction process (15) as a function of the integer N2, comprising, if N2 ≥ 1, predicting as a stroke prediction, based on features of the N1 most recent first points (PT1c), N2 next points (PT2) that are expected to be displayed subsequently to the N1 most recent first points.