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arXiv AI By Sujith Subramanian, P N Bhargav Teja, Susen Sarkar, Rajasekhara Reddy Duvvuru Muni

Benchmarking Embedding Geometries and Clustering Algorithms for Unsupervised Patent Claim Classification

Read the original on arXiv AI →

The paper evaluates unsupervised embedding geometries for patent claim classification, comparing hyperbolic and Euclidean Variational Autoencoders trained on 45,000 US patents. Hyperbolic embeddings retrieve cross-category prior art 52% more often than Euclidean embeddings (18.76% vs. 12.34% of top‑10 results across 3,000 queries). The study also links classification breadth and patents per subgroup to model performance, offering a practical signal for when the method is most useful.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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