8 research outputs found
Tracing technological development trajectories: A genetic knowledge persistence-based main path approach
The aim of this paper is to propose a new method to identify main paths in a
technological domain using patent citations. Previous approaches for using main
path analysis have greatly improved our understanding of actual technological
trajectories but nonetheless have some limitations. They have high potential to
miss some dominant patents from the identified main paths; nonetheless, the
high network complexity of their main paths makes qualitative tracing of
trajectories problematic. The proposed method searches backward and forward
paths from the high-persistence patents which are identified based on a
standard genetic knowledge persistence algorithm. We tested the new method by
applying it to the desalination and the solar photovoltaic domains and compared
the results to output from the same domains using a prior method. The empirical
results show that the proposed method overcomes the aforementioned drawbacks
defining main paths that are almost 10x less complex while containing more of
the relevant important knowledge than the main path networks defined by the
existing method.Comment: 20 pages, 7 figure
Tracing technological development trajectories: A genetic knowledge persistence-based main path approach
The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method can dramatically reduce network complexity without missing any dominantly important patents. The main paths identified by our approach for two test cases are almost 10x less complex than the main paths identified by the existing approach. The proposed approach identifies all dominantly important patents on the main paths, but the main paths identified by the existing approach miss about 20% of dominantly important patents.This work was supported by Hanyang University (Grant number: HY-2016, http://www.hanyang.ac.kr) and Singapore University of Technology and Design (Grant number: 6921538, http://www.sutd.edu.sg).
We would like to thank Hanyang University and SUTD/MIT International Design Center for supporting the research
A Convolutional Neural Network-based Patent Image Retrieval Method for Design Ideation
The patent database is often used in searches of inspirational stimuli for
innovative design opportunities because of its large size, extensive variety
and rich design information in patent documents. However, most patent mining
research only focuses on textual information and ignores visual information.
Herein, we propose a convolutional neural network (CNN)-based patent image
retrieval method. The core of this approach is a novel neural network
architecture named Dual-VGG that is aimed to accomplish two tasks: visual
material type prediction and international patent classification (IPC) class
label prediction. In turn, the trained neural network provides the deep
features in the image embedding vectors that can be utilized for patent image
retrieval and visual mapping. The accuracy of both training tasks and patent
image embedding space are evaluated to show the performance of our model. This
approach is also illustrated in a case study of robot arm design retrieval.
Compared to traditional keyword-based searching and Google image searching, the
proposed method discovers more useful visual information for engineering
design.Comment: 11 pages, 11 figure