Revealing Structural Patterns of Patent Citation by a Two-Boundary Network Model Based on USPTO Data

Abstract

The patent is one of the carriers of scientific research and development, and an indicator of technical innovation. As a promising approach for modeling complex systems, complex networks could provide the sound theoretical framework for developing proper simulation models. Many researchers use the relations of patent citations and transfers to study knowledge propagate and output in the network. However, knowledge flow in patents should be fully considered by substantial and fruitful connections, both in the process of knowledge application and knowledge output. In this paper, we present a two-boundary network model with knowledge application boundary and knowledge output boundary to reveal the patent citation patterns in knowledge flow. The feasibility and effectiveness of the two-boundary network model are proved with theory and experiment. Utilizing 578,678 patents from the United States Patent and Trademark Office between 2015 and 2018 with the two-boundary network model, we put up a fixed effect ordinary least square equation to reveal the patent impacts of different structural patterns. Experimental results show that, in the perspective of structural patterns, the highly impacted patents without assigning are greatly influenced by their scientific literature references and provide knowledge for other assigned patents. However, considering all the fixed effect factors, patents that transfer knowledge from other patents to assigned patents are more likely to become highly impacted patents. Besides, we find the two-boundary network model fits the real patent knowledge flow well by comparing it with the other models. INDEX TERMS Two-boundary network model, knowledge flow, regression equation, fixed effect.

Exploring the topology and dynamic growth properties of co-invention networks and technology fields

“…In a way, what we found here is that the patent system appears to restrict knowledge spillover, interaction, and collaboration (and therefore the sharing of knowledge and scientific advances) and thus call into question the traditional economic argument in favor of patent protection. This basic finding is in line with recent research and suggests that in a world in which innovations are largely assigned to private businesses, corporate and intellectual property restrictions appear to have a significant network effect on how inventors interact [ 26 , 37 ]. The results of the present study, including our findings related to the degree distribution of inventor networks in young and mature industries, provide further evidence in support of this idea.…”

“…To answer these questions, we study changes in the structural configurations of eight technology fields (IPC) via the power-law, small-world, preferential attachment, shrinking diameter, densification law, and ‘gelling point’ hypotheses. Similar to the existing literature [ 22 – 26 ], we obtain mixed results. Based on network statistics, we argue that the sudden rise of giant networks in six sectors can be understood as a phase transition in which small, isolated networks form one giant component.…”

“…Patent collaboration is a complex task and company policies seem to play a decisive role, especially in shaping the partner selection of inventor teams and therefore collaboration networks. This is increasingly the case as the increasing regulatory patent protection system tends to restrict rather than advance knowledge spillover and sharing [ 26 ].…”

Determinants of highly-cited green patents: The perspective of network characteristics

“…Therefore, this study adopts the method of network analysis, using patents as nodes and connecting the flow of technical knowledge between patents to build a patent citation network, which measures the network position and network structure of patents. Bessen [ 4 ] finds that highly-cited patents are more valuable, besides, highly-cited patents also contained important technological advances [ 13 , 14 ]. Therefore, this study used four indicators of network characteristic: degree centrality, closeness centrality, eigenvector centrality, and network structure, to explore the relationship between network characteristic and highly-cited patents to fill this research gap.…”

A Two-Layer Network Model Reveals the Adhesion Scientist Career Stage and Research Topic in China

“…The increasing availability of scholar data-publications, research funding, collaborations, paper citations, and scientist mobility-provides unprecedented data resources to explore complex network structures and evolutions of sciences [1], [2]. Scientific thought mainly refers to the concepts and relations in scientific research achievements, these elements can be connected with each other in various ways, so the science can be described as a complex and developing network [1].…”