Font recommendation, topic extraction, and training data generation
AssigneeAdobe Inc.
InventorsPeter Evan O'Donovan, Siddartha Reddy Turpu, Razvan Cotlarciuc, Oliver Markus Michael Brdiczka, Nipun Jindal, Costin-Stefan Ion
Life of this patent
Estimating the term…
Abstract
Font recommendation techniques are described that provide recommendations of fonts based on a variety of factors, automatically and without user intervention in real time. This is performable in a variety of ways by addressing a wide range of considerations as part of machine learning, examples of which include context, popularity, similarity, customization, and topic compatibility.
Claims20
A method comprising:
- receiving, by a processing device, an item of digital content;
- generating, by the processing device, a font representation of a font included in the item of digital content, the generating including encoding the font using machine learning;
- determining, by the processing device, similarity of the font representation to a plurality of said font representations;
- producing, by the processing device, a font recommendation based on the determined similarity, the font recommendation including fonts that are dissimilar to each other;
- and
- outputting, by the processing device, the recommendation.
One or more computer-readable storage media storing instructions that, responsive to execution by a processing device, causes the processing device to perform operations comprising:
- receiving an item of digital content;
- extracting a topic from the digital content;
- generating a font representation of a font included in the digital content, the generating including encoding the font using machine learning;
- and
- producing a font recommendation based on the generated font representation and the extracted topic using a machine-learning model.
A computing device comprising:
- a processing device;
- and
- a computer-readable storage media storing instructions that, responsive to execution by the processing device, causes the processing device to perform operations including:
- obtaining a plurality of items of digital content corresponding to a user identifier (ID);
- forming training data for the user ID, the forming including encoding fonts included within the plurality of digital content using machine learning and detecting at least one font feature describing how glyphs of the font are included as part of the plurality of digital content, respectively;
- and
- configuring a machine-learning model to generate a customized font recommendation for the user ID based on font context by training the machine-learning model using the training data.
Dependencies are inferred from the claim text.