Automating Claim Construction in Patent Applications: The CMUmine Dataset

Ozan Tonguz, Yiwei Qin, Yimeng Gu, Hyun Hannah Moon


Abstract
Intellectual Property (IP) in the form of issued patents is a critical and very desirable element of innovation in high-tech. In this position paper, we explore the possibility of automating the legal task of Claim Construction in patent applications via Natural Language Processing (NLP) and Machine Learning (ML). To this end, we first create a large dataset known as CMUmine™and then demonstrate that, using NLP and ML techniques the Claim Construction in patent applications, a crucial legal task currently performed by IP attorneys, can be automated. To the best of our knowledge, this is the first public patent application dataset. Our results look very promising in automating the patent application process.
Anthology ID:
2021.nllp-1.21
Volume:
Proceedings of the Natural Legal Language Processing Workshop 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Editors:
Nikolaos Aletras, Ion Androutsopoulos, Leslie Barrett, Catalina Goanta, Daniel Preotiuc-Pietro
Venue:
NLLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
205–209
Language:
URL:
https://aclanthology.org/2021.nllp-1.21
DOI:
10.18653/v1/2021.nllp-1.21
Bibkey:
Cite (ACL):
Ozan Tonguz, Yiwei Qin, Yimeng Gu, and Hyun Hannah Moon. 2021. Automating Claim Construction in Patent Applications: The CMUmine Dataset. In Proceedings of the Natural Legal Language Processing Workshop 2021, pages 205–209, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Automating Claim Construction in Patent Applications: The CMUmine Dataset (Tonguz et al., NLLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.nllp-1.21.pdf