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Prior Art Search Using Multi-Modal Embedding of Patent Documents
- Kang, Myungchul;
- Lee, Suan;
- Lee, Wookey
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8초록
Due to the limitations of the existing prior art search methods, a new patent search paradigm can be innovated by the concepts based on a precise patent document embedding, and a real-time feedback. These concepts can be achieved by the following ideas. The latest language model BERT can be incorporated with the description drawing embedding so that the explorable user interactive model can be adopted to the patent domain for "Building an artificial intelligent patent search system." Therefore, these methodologies mainly with the help of deep learning can solve the traditional labor-intensive and time-consuming prior art search.
키워드
prior art search; language model; multi-modal embedding; explorable user-interactive model; QUALITY
- 제목
- Prior Art Search Using Multi-Modal Embedding of Patent Documents
- 저자
- Kang, Myungchul; Lee, Suan; Lee, Wookey
- 발행일
- 2020
- 유형
- Proceedings Paper
- 저널명
- 2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (BIGCOMP 2020)
- 페이지
- 548 ~ 550