Towards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification

Xiaochen Gao, Zhaoyi Hou, Yifei Ning, Kewen Zhao, Beilei He, Jingbo Shang, Vish Krishnan


Abstract
Predicting the approval chance of a patent application is a challenging problem involving multiple facets. The most crucial facet is arguably the novelty — 35 U.S. Code § 102 rejects more recent applications that have very similar prior arts. Such novelty evaluations differ the patent approval prediction from conventional document classification — Successful patent applications may share similar writing patterns; however, too-similar newer applications would receive the opposite label, thus confusing standard document classifiers (e.g., BERT). To address this issue, we propose a novel framework that unifies the document classifier with handcrafted features, particularly time-dependent novelty scores. Specifically, we formulate the novelty scores by comparing each application with millions of prior arts using a hybrid of efficient filters and a neural bi-encoder. Moreover, we impose a new regularization term into the classification objective to enforce the monotonic change of approval prediction w.r.t. novelty scores. From extensive experiments on a large-scale USPTO dataset, we find that standard BERT fine-tuning can partially learn the correct relationship between novelty and approvals from inconsistent data. However, our time-dependent novelty features offer a boost on top of it. Also, our monotonic regularization, while shrinking the search space, can drive the optimizer to better local optima, yielding a further small performance gain.
Anthology ID:
2022.acl-long.28
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
349–372
Language:
URL:
https://aclanthology.org/2022.acl-long.28
DOI:
10.18653/v1/2022.acl-long.28
Bibkey:
Cite (ACL):
Xiaochen Gao, Zhaoyi Hou, Yifei Ning, Kewen Zhao, Beilei He, Jingbo Shang, and Vish Krishnan. 2022. Towards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 349–372, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Towards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification (Gao et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.28.pdf