Task Extraction from Inked Content Using Layout-Aware Sequence Labeling
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Solution Overview
Problem
Current digital whiteboard and video conferencing tools face challenges in automating the extraction of tasks from inked content on digital canvases, as inked content often lacks conventional sentence and punctuation structures, leading to inaccurate task extraction due to its informal and irregular formatting.
Innovation Solution
A computer system with a task extraction subsystem that uses sequence labeling machine learning models, such as RoBERTa, to segment and classify sentences from inked content by leveraging document layout features like line breaks and bullet points, enabling accurate extraction of task sentences with low latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sentence segmentation and classification approaches are used on inked content, then the system can process text data, but the extraction accuracy is low due to irregular formatting and lack of conventional sentence structures
Solution Approach 1:
The patent transforms the input data parameters by converting inked content into structured representations with explicit sentence boundary markers and hierarchical organization. This parameter transformation enables conventional NLP models to process informal inked content by changing its structural parameters from unstructured strokes to structured text with punctuation and formatting cues.
Solution Approach 2:
The system introduces an intermediary processing layer that converts inked content into a structured intermediate representation before feeding it to task extraction models. This intermediary step includes recognizing writing regions, determining sentence boundaries, and formatting text with punctuation, thereby bridging the gap between informal inked content and formal NLP processing requirements.
2Productivity
If manual task extraction from inked content is performed, then high accuracy can be achieved, but the process is time-consuming and lacks automation
Solution Approach 1:
The system performs preliminary processing of inked content by automatically recognizing writing regions, determining sentence boundaries, and formatting text before task extraction. This preliminary structuring action enables subsequent automated task extraction to proceed efficiently with accuracy comparable to manual processing, thereby improving productivity while maintaining automation.
Solution Approach 2:
The patent segments inked content into distinct writing regions and further segments them into sentence-level units with identified boundaries. This multi-level segmentation transforms continuous inked content into discrete, processable units that can be efficiently handled by automated task extraction systems, improving both speed and automation extent.
3Measurement precision
If inked content is processed without sentence boundary detection, then processing is simpler, but task extraction accuracy deteriorates due to inability to distinguish complete thoughts
Solution Approach 1:
The system performs preliminary sentence boundary detection and marking before task classification. By identifying complete sentences in advance and marking their boundaries, the system enables accurate task-level classification without requiring complex real-time analysis during the classification process itself.
Solution Approach 2:
The patent introduces sentence boundary markers and structural intermediaries that indicate where complete thoughts begin and end. These intermediary elements serve as explicit cues for task extraction models, improving sentence-level classification accuracy without requiring the model to independently determine sentence boundaries during processing.
Data Source
AI summary
A computer system is provided that includes one or more processors configured to receive user input for inked content to a digital canvas, and process the inked content to determine one or more writing regions. Each writing region includes recognized text and one or more document layout features associated with that writing region. The one or more processors are further configured to tokenize a target writing region of the one or more writing regions into a sequence of tokens, process the sequence of tokens of the target writing region using a task extraction subsystem that operates on tokens representing both the recognized text and the one or more document layout features of the target writing region, segment the target writing region into one or more sentence segments, and classify each of the one or more sentence segments as a task sentence or a non-task sentence.