Filtering patent maps for visualization of diversification paths of inventors and organizations

Abstract

In the information science literature, recent studies have used patent databases and patent classification information to construct network maps of patent technology classes. In such a patent technology map, almost all pairs of technology classes are connected, whereas most of the connections between them are extremely weak. This observation suggests the possibility of filtering the patent network map by removing weak links. However, removing links may reduce the explanatory power of the network on inventor or organization diversification. The network links may explain the patent portfolio diversification paths of inventors and inventing organizations. We measure the diversification explanatory power of the patent network map, and present a method to objectively choose an optimal tradeoff between explanatory power and removing weak links. We show that this method can remove a degree of arbitrariness compared with previous filtering methods based on arbitrary thresholds, and also identify previous filtering methods that created filters outside the optimal tradeoff. The filtered map aims to aid in network visualization analyses of the technological diversification of inventors, organizations, and other innovation agents, and potential foresight analysis. Such applications to a prolific inventor (Leonard Forbes) and company (Google) are demonstrated.

Design opportunity conception using the total technology space map

“…These studies normally utilize an entire patent database to construct network maps of all the technology domains defined in patent classification systems (Kay et al , 2014; Leydesdorff et al , 2014). Such network maps have been used to analyze the patent portfolio diversification of individual inventors (Alstott et al , 2017 b ), firms (Yan and Luo, 2017 a ), regions (Boschma et al , 2015), and the design space evolution of a system product (Song et al , 2016), but not yet to support design ideation. The aim of such maps to cover the entire technology space determines the nodes on the maps are technology domains (i.e., aggregations of technologies) and makes it not useful to inspire designers for concrete technical solutions.…”

Data-Driven Network Visualization for Innovation and Competitive Intelligence

“…The technology space network includes all the 122 meaningfully defined 3-digit international patent classes (e.g., combust engine, nanotechnology) to have the most comprehensive coverage of the conceptual total technology space. Although one can also use 4 to 7 digit patent classes, prior studies have suggested 3-digit classes are the most suitable and stable representations of technology domains [17] and provide the best map resolution and ease for visual analytics [18]. These patent classes as network nodes are linked and positioned according to the knowledge proximity among represented technology domains.…”

“…Despite the existence of other measures of the relationships between patent categories [11], [28], [29], this metric is chosen for its high explanatory power on firms' historical diversifications across technology domains according to our statistical analysis [18]. To ensure statistical significance, more than 6 million granted patents in the USPTO database are used to calculate the knowledge proximity between each pair of the 122 technology domains.…”

“…The methodology locates the technology positions of a firm as a subspace of the total technology space, and then assess and compare the subspaces of different firms. We operationalize the conceptual "total technology space" as an empirically constructed network map of all patent technology classes defined by World Intellectual Property Office (WIPO) in line with a few recent studies on patent mapping [16]- [18]. The concept of technology positions of a firm is operationalized as the patent classes where the firm's patents have been assigned.…”

Patent Analysis of the Critical Technology Network of Semiconductor Optical Amplifiers

“…Patents are the most direct evidence for measuring innovation output and can be used as indicators to observe technology development tendencies [10][11][12]. Consequently, investigations of industry-academia technology collaboration outcomes [13,14] or the related studies of the industrial technologies can be conducted using patents [15,16]. Thus, patent information can be used as one of the most direct indicators of technology development.…”