Dynamic Font Similarity System for New Fonts
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Solution Overview
Problem
Content designers face difficulties in identifying fonts similar to a given font, especially with newly released fonts, as static lists of pre-determined font representations cannot account for these new fonts, limiting the discovery of better fitting fonts for design purposes.
Innovation Solution
A font similarity system that dynamically updates a supplemental list of font representations, generating representations for newly installed fonts using techniques like convolutional neural networks, allowing for similarity comparisons regardless of pre-computed lists, while conserving computing resources by utilizing pre-computed lists for existing fonts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static list of pre-determined font representations is used, then resource consumption is reduced, but the system cannot account for newly released fonts
Solution Approach 1:
The system transitions from a static font representation list to a dynamic hybrid architecture that combines pre-computed representations with on-demand generation. When new fonts are detected, the system dynamically generates their representations using convolutional neural networks, while existing fonts continue to use pre-computed representations. This dynamic adaptation resolves the contradiction by making the system versatile for new fonts without unnecessarily consuming resources for existing fonts.
Solution Approach 2:
The font representation system is segmented into two distinct components: a pre-computed list for existing fonts and a supplemental list generated on-demand for new fonts. This segmentation allows the system to optimize resource usage by using the efficient pre-computed approach for the majority of fonts while providing adaptive generation capability for new fonts, thus resolving the contradiction between resource efficiency and adaptability.
2Adaptability or versatility
If font representations are pre-computed for all fonts, then similarity comparisons are fast, but newly released fonts cannot be included
Solution Approach 1:
The system performs preliminary action by pre-computing font representations for existing fonts before they are needed for comparison. This allows the system to maintain fast similarity comparison performance for the majority of fonts. When new fonts are released, the system generates their representations on-demand, accepting a one-time computational delay that does not affect future comparison speed for these fonts.
Solution Approach 2:
The system changes the state parameter of font representations from a static pre-computed state to a dynamic state that can be generated on-demand. For new fonts, the representation parameter is generated when needed rather than pre-computed, allowing the system to include newly released fonts while maintaining fast comparison performance for the pre-computed portion of the font library.
3Reliability
If a dynamic supplemental list is created for new fonts, then all fonts can be compared, but computing resources are consumed for generation
Solution Approach 1:
The system implements self-service by automatically detecting newly installed fonts and generating their representations on-demand without requiring manual intervention. The font representation generation is triggered automatically when a new font is detected, ensuring complete font similarity search capability while consuming computing resources only when necessary, thus achieving reliability without excessive resource consumption.
Solution Approach 2:
The system uses feedback mechanisms to monitor the font library for newly installed fonts and triggers representation generation only when changes are detected. This feedback-driven approach ensures that the supplemental list is updated maintain completeness and reliability of font similarity search while avoiding unnecessary computation for fonts that have not changed, thereby optimizing resource consumption.
Data Source
AI summary
Embodiments of the present invention are directed at providing a font similarity system. In one embodiment, a new font is detected on a computing device. In response to the detection of the new font, a pre-computed font list is checked to determine whether the new font is included therein. The pre-computed font list including feature representations, generated independently of the computing device, for corresponding fonts. In response to a determination that the new font is absent from the pre-computed font list, a feature representation for the new font is generated. The generated feature representation capable of being utilized for a similarity analysis of the new font. The feature representation is then stored in a supplemental font list to enable identification of one or more fonts installed on the computing device that are similar to the new font. Other embodiments may be described and/or claimed.