Patent Representation Learning via Self-supervision

We study self-supervised patent representation learning with contrastive objectives.

A standard baseline constructs positives by encoding the same text twice under independent dropout masks, but applying this recipe to long, structured patent documents requires careful calibration.

We show that dropout-only training can be substantially strengthened by tuning temperature and dropout rate, yet its best configuration is evaluation-dependent and does not transfer uniformly from title--abstract retrieval to claim-to-disclosure retrieval.

We propose mixed dropout--section positives, a patent-specific view construction strategy in which the anchor is the title--abstract view and the positive is sampled either from a dropout re-encoding of the same view or from another section of the same patent, such as claims, summary, background, drawings, or description.

This uses patent-internal structure as a training-time signal without IPC labels, citations, or relevance annotations.

We evaluate on graded EPO search-report retrieval, DAPFAM, a recently proposed family-level patent retrieval benchmark, and IPC subclass classification.

Section-based positives improve over calibrated dropout-only and generic title--abstract augmentation baselines, are competitive with citation-informed patent encoders and a general-purpose embedding model, and perform strongly on the out-of-domain split of DAPFAM.

Additional cross-section alignment diagnostics show that section-pair training improves compatibility among abstracts, claims, and descriptions of the same invention.

These results indicate that patent sections provide effective self-supervised positive views for learning dense patent representations.