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
- 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)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2021.nllp-1.21.pdf