关键词: 创新设计, 专利推荐, 专利知识挖掘, 命名实体识别

Abstract: To address the challenges of insufficient knowledge mining and low efficiency of knowledge reuse in the innovative design of complex industrial products, a complex product patent knowledge mining and recommendation method based on few-shot feature extraction and cross-citation network is proposed. Firstly, patent knowledge model is constructed by extracting three types of featured entities including domain(D), function(F), and technology(T), which is represented as a three-aspect model <D, F, T>. Meanwhile, the annotation rule of few-shot patent dataset is proposed for manual feature annotation. Secondly, a fine-tuned BERT model is constructed based on a few-shot annotated patent dataset to achieve automatic patent feature extraction. A graph neural network is employed to learn the representation of the patent co-citation network. Thirdly, the fine-tuned BERT model is subsequently used to extract feature entities in the input design problem or task description, which are vectorized and their cosine similarities computed to generate a recommendation list. Finally, a prototype based on Django framework is also developed, which realizes the two functions of feature extraction and knowledge push, and illustrates the use scenarios of this method based on a case of automobile windshield wiper strip design.

Key words: innovative design, patent recommendation, patent knowledge mining, named entity recognition