关键词: 产品创新, 专利数据, 深度学习, 知识需求情境, 知识资源推荐

Abstract: Patent contains the most complete design information in most domains, it can provide designers with valuable guidance in solving design problems. Aiming at the problem that the existing patent recommendation methods are difficult to effectively recommend cross-domain patents, a cross-domain patent knowledge recommendation method based on deep learning is proposed for the conceptual design of innovative products. The product function and knowledge demand situation are modeled, the design problem is standardized, and the design problem space is generated. A semi-supervised learning algorithm（TG-TCI） is proposed to automatically classify and label patent function information according to the function base, and use entity recognition algorithm（BERT-BiLSTM-CRF） to extract patent application scenario terms and technical terms, combined with international patent classifications（IPC） information is used to represent the function, context, technology and domain attributes of patents, thereby generating patent knowledge space. Find the required cross-domain patents through the functional base and knowledge context mapping from the design problem space to the patent knowledge space, cluster and evaluate them according to the technology and domain attributes, and select specific patents to stimulate the creativity of designers. An actual case is used to analyze and verify the feasibility and effectiveness of the patent knowledge recommendation model based on deep learning.

Key words: product innovation, patent data, deep learning, knowledge demand situation, knowledge resource recommendation