Ink Stroke Metadata Generation Using Inclusion Relations

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Solution Overview

Problem

Existing ink data generation methods face challenges in handling metadata efficiently, particularly in assigning and managing meta-information for sets of strokes.

Innovation Solution

An ink data generation apparatus and method that includes a first acquisition section to identify strokes without meta-information, a second acquisition section to identify strokes with meta-information, a set determination section to determine inclusion relations, and a data generation section to generate metadata based on these relations, facilitating improved handling of ink data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If metadata is manually assigned to each stroke set, then completeness of meta-information can be ensured, but time consumption and user burden increase significantly

Engineering Contradiction:

Improvecompleteness of meta-informationVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs automatic metadata generation by having the stroke data itself provide the information needed for metadata creation. The inclusion relationship detection algorithm automatically determines hierarchy by analyzing spatial and temporal relationships between strokes, eliminating the need for manual user input while ensuring complete meta-information assignment

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system pre-processes stroke data to detect inclusion relationships and generates metadata structures before final output. By performing the metadata generation operation in advance during the ink data processing workflow, the system avoids time-consuming manual assignment later while maintaining information completeness

Inventive Principle:

Principle #10Preliminary action

2Ease of operation

If metadata structure is simplified for easier handling, then ease of operation improves, but the ability to represent complex inclusion relations between stroke sets deteriorates

Engineering Contradiction:

Improveease of handling ink dataVSAvoidinclusion relation information

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The metadata structure is segmented into hierarchical levels corresponding to different inclusion relationship depths. Each stroke set can have metadata indicating its position in the hierarchy (parent-child relationships), allowing complex nested structures to be represented through repeated application of simple inclusion relation markers rather than requiring a single complex structure

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system represents inclusion relations by adding a hierarchical dimension to the metadata structure. Instead of using complex horizontal relationships, the inclusion hierarchy is expressed through vertical nesting of metadata elements, where each level represents a different scope of stroke inclusion, making the structure both simple and information-rich

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

3Productivity

If automatic metadata generation is implemented, then productivity improves, but the complexity of the data processing system increases

Engineering Contradiction:

Improvemetadata generation efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system replaces manual mechanical metadata assignment with an automated algorithmic process. The inclusion relationship detection uses computational geometry and temporal analysis to automatically determine hierarchy, substituting human cognitive processing with machine-based automated reasoning that increases productivity while managing complexity through standardized algorithms

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the parameter of metadata generation from manual input-based to algorithm-based automatic generation. By transforming the generation mechanism from human-operated to system-autonomous, productivity increases significantly. The complexity is managed by using well-defined parameters for inclusion detection (spatial overlap, temporal sequence) rather than requiring complex user judgments

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12511034B2Ink data generation apparatus, method, and program

Publication Date: 2025.12.30 WACOM CO LTD

AI summary

Provided are an ink data generation apparatus, an ink data generation method, and an ink data generation program that are capable of improving ease of handling ink data when or after metadata is generated. An ink data generation apparatus performs a determination as to an inclusion relation between a first set and a second set by comparing stroke elements of first set data and stroke elements of second set data using the first set data and the second set data. The ink data generation apparatus generates first metadata for the first set described in a form that varies in accordance with a result of the determination.