Drawing From Basic Elements TRIZ Cases

Neural networks translate plain-English voice commands into 3D perspectives that balance broad structure with detailed data visibility.

This case converts emoji images into dot matrix fonts, helping memory- and performance-limited wearables display visual content.

This case uses structural graphs to order overlapping surfaces and generate intertwined designs with minimal user interaction.

This case uses adjacent lane edges to set reference points and directions, enabling detailed non-link map element rendering.

This case replaces manual axis drawing and coding with scale-bound graphics that update as datasets change.

Fine-grained graph redaction handles policy exceptions before export, helping entities share authorized data while preserving provenance.

Drag-and-drop axis assignment and server-side processing simplify accurate, real-time graphing of large datasets.

Co-occurrence analysis trims large network diagrams to fit display size.

Reference figures define scoring boundaries for Cartesian drawings, improving scoring flexibility and measurement of understanding.