TY - JOUR

T1 - AI for Patents

T2 - A Novel Yet Effective and Efficient Framework for Patent Analysis

AU - Son, Junyoung

AU - Moon, Hyeonseok

AU - Lee, Jeongwoo

AU - Lee, Seolhwa

AU - Park, Chanjun

AU - Jung, Wonkyung

AU - Lim, Heuiseok

N1 - Publisher Copyright: © 2013 IEEE.

PY - 2022

Y1 - 2022

N2 - Patents provide inventors exclusive rights to their inventions by protecting their intellectual property rights. However, analyzing patent documents generally requires knowledge of various fields, considerable human labor, and expertise. Recent studies to alleviate this problem on patent analysis deal only with the analysis of claims and abstract parts, neglecting the descriptions that contain essential technical cores. Moreover, few studies use a deep learning approach to handle the entire patent analysis process, including preprocessing, summarization, and key-phrase generation. Therefore, we propose a novel multi-stage framework that can aid in analyzing patent documents by using the description part of the patent rather than abstracts or claims with deep learning. The framework comprises two stages: key-sentence extraction and key-phrase generation tasks. These stages are based on the T5 model structure, transformer-based architecture that uses a text-to-text approach. To further improve the framework's performance, we employed two key factors: i) post-training the model with a patent-related raw corpus for encouraging the model's comprehension of the patent domain, and ii) utilizing a text rank algorithm for efficient training based on the priority score of each sentence. We verified that our key-phrase generation method of the framework shows higher performance in both superficial and semantic evaluation than other extraction methods. In addition, we provided the validity and effectiveness of our methods through quantitative and qualitative analysis, demonstrating the practical functionality of our methods. We also provided a practical contribution to the patent analysis by releasing the framework as a demo system.

AB - Patents provide inventors exclusive rights to their inventions by protecting their intellectual property rights. However, analyzing patent documents generally requires knowledge of various fields, considerable human labor, and expertise. Recent studies to alleviate this problem on patent analysis deal only with the analysis of claims and abstract parts, neglecting the descriptions that contain essential technical cores. Moreover, few studies use a deep learning approach to handle the entire patent analysis process, including preprocessing, summarization, and key-phrase generation. Therefore, we propose a novel multi-stage framework that can aid in analyzing patent documents by using the description part of the patent rather than abstracts or claims with deep learning. The framework comprises two stages: key-sentence extraction and key-phrase generation tasks. These stages are based on the T5 model structure, transformer-based architecture that uses a text-to-text approach. To further improve the framework's performance, we employed two key factors: i) post-training the model with a patent-related raw corpus for encouraging the model's comprehension of the patent domain, and ii) utilizing a text rank algorithm for efficient training based on the priority score of each sentence. We verified that our key-phrase generation method of the framework shows higher performance in both superficial and semantic evaluation than other extraction methods. In addition, we provided the validity and effectiveness of our methods through quantitative and qualitative analysis, demonstrating the practical functionality of our methods. We also provided a practical contribution to the patent analysis by releasing the framework as a demo system.

KW - Deep learning

KW - Key-sentence extraction

KW - Keyword extraction

KW - Patent

KW - Patent analysis

KW - Post training

UR - http://www.scopus.com/inward/record.url?scp=85130817030&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2022.3176877

DO - 10.1109/ACCESS.2022.3176877

M3 - Journal article

AN - SCOPUS:85130817030

VL - 10

SP - 59205

EP - 59218

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -