Canva Files Patent on Multi-Label Classification Model Training
Summary
Canva Pty Ltd filed US Patent Application US20260099771A1 for methods and systems to train multi-label classification models to incorporate new entities added to a model dictionary. The method identifies candidate training instances based on the new entity, generates training records with the new label, and refines label sets to remove unrelated entries before model training. The application was published April 9, 2026, with a filing date of September 2, 2025.
What changed
Canva Pty Ltd filed patent application US20260099771A1 disclosing methods for training multi-label classification models to learn new entities added to a model dictionary. The disclosed invention involves identifying candidate training instances for new entities, generating training records with the new entity as a label, adding existing entities to expand label sets, and refining labels to remove unrelated entries before model training.\n\nTechnology companies developing machine learning classification systems should monitor this application's prosecution and eventual grant. If the patent issues with broad claims, developers of multi-label classification systems may need to consider licensing or design-around strategies to avoid potential infringement claims in AI/ML product development.
What to do next
- Monitor for patent grant updates
- Review claims for potential licensing implications
Archived snapshot
Apr 11, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Systems and methods for training a multi-label classification model
Application US20260099771A1 Kind: A1 Apr 09, 2026
Assignee
Canva Pty Ltd
Inventors
Kerry Jayne HALUPKA, Benjamin Phillip ALEXANDER
Abstract
A method, system and computer-readable medium for training a multi-label classification model to learn a new entity added to a dictionary of the multi-label classification model are disclosed. The method includes: identifying candidate training instances based on the new entity; generating a training record for each of the candidate training instances, where the training record includes the candidate training instance and the new entity as a new label; for each training record: adding existing entities from the dictionary, such that the training record includes a set of labels including the new label; and refining the set of labels to remove labels that are not related to the corresponding training instance. The method further includes training the multi-label classification model using the refined training records.
CPC Classifications
G06N 20/00
Filing Date
2025-09-02
Application No.
19316156
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