Recent works in the information science literature have presented cases of using patent databases and patent classification information to construct network maps of technology fields, which aim to aid in competitive intelligence analysis and innovation decision making.Constructing such a patent network requires a proper measure of the distance between different classes of patents in the patent classification systems. Despite the existence of various distance measures in the literature, it is unclear how to consistently assess and compare them, and which ones to select for constructing patent technology network maps.This ambiguity has limited the development and applications of such technology maps.Herein, we propose to compare alternative distance measures and identify the superior ones by analyzing the differences and similarities in the structural properties of resulting patent

Technology is a complex system, with technologies relating to each other in a space that can be mapped as a network. The technology network's structure can reveal properties of technologies and of human behavior, if it can be mapped accurately. Technology networks have been made from patent data, using several measures of proximity. These measures, however, are influenced by factors of the patenting system that do not reflect technologies or their proximity. We introduce a method to precisely normalize out multiple impinging factors in patent data and extract the true signal of technological proximity, by comparing the empirical proximity measures with what they would be in random situations that remove the impinging factors. With this method, we created technology networks, using data from 3.9 million patents. After normalization, different measures of proximity became more correlated with each other, approaching a single dimension of technological proximity. The normalized technology networks were sparse, with few pairs of technology domains being significantly related. The normalized network corresponded with human behavior: we analyzed the patenting histories of 2.8 million inventors and found they were more likely to invent in two different technology domains if the pair was closely related in the technology network. We also analyzed 250 thousand firms' patents and found that, in contrast, firms' inventive activities were only modestly associated with the technology network; firms' portfolios combined pairs of technology domains about twice as often as inventors. These results suggest that controlling for impinging factors provides meaningful measures of technological proximity for patent-based mapping of the technology space, and that this map can be used to aid in technology innovation planning and management.

Many edge prediction methods have been proposed, based on various local or global properties of the structure of an incomplete network. Community structure is another significant feature of networks: Vertices in a community are more densely connected than average. It is often true that vertices in the same community have "similar" properties, which suggests that missing edges are more likely to be found within communities than elsewhere. We use this insight to propose a strategy for edge prediction that combines existing edge prediction methods with community detection. We show that this method gives better prediction accuracy than existing edge prediction methods alone.

Engineers and technology firms must continually explore new design opportunities and directions to sustain or thrive in technology competition. However, the related decisions are normally based on personal gut feeling or experiences. Although the analysis of user preferences and market trends may shed light on some design opportunities from a demand perspective, design opportunities are always conditioned or enabled by the technological capabilities of designers. Herein, we present a data-driven methodology for designers to analyze and identify what technologies they can design for the next, based on the principle—what a designer can currently design condition or enable what it can design next. The methodology is centered on an empirically built network map of all known technologies, whose distances are quantified using more than 5 million patent records, and various network analytics to position a designer according to the technologies that they can design, navigate technologies in the neighborhood, and identify feasible paths to far fields for novel opportunities. Furthermore, we have integrated the technology space map, and various map-based functions for designer positioning, neighborhood search, path finding, and knowledge discovery and learning, into a data-driven visual analytic system named InnoGPS. InnoGPS is a global position system (GPS) for finding innovation positions and directions in the technology space, and conceived by analogy from the GPS that we use for positioning, neighborhood search, and direction finding in the physical space.

This paper describes interfacial crystalline structures found in injection overmolded polypropylene components and the relationship of these structures to bond strength between the components. The combined effects of the development of hierarchical gradient structures and the particular thermomechanical environment near the interface on the interfacial crystalline structures were investigated in detail by PLM, SEM, DSC, WAXD, and infrared dichroism spectroscopy. The experimental results showed that during molding there was competitive formation of interfacial crystalline structures consisted of "shish-kebab" layer (SKL) and a transcrystalline layers (TCL). Variation in shear stress (controlled by injection pressure and injection speed) plays an important role in the formation of the SKL. The formation of TCL is influenced by the thermal environment, namely melt temperature and mold temperature. Increasing within certain limits, interfacial temperature and the thermal gradient near the interface promotes β-iPP growth. The relationship between interfacial crystalline structures and interfacial bond strength was established by lap shear measurement. The interfacial bond strength is improved by enhancing the formation of TCL, but reduced if SKL predominates.

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The Cooperative Patent Classifications (CPC) recently developed cooperatively by the European and US Patent Offices provide a new basis for mapping patents and portfolio analysis. CPC replaces International Patent Classifications (IPC) of the World Intellectual Property Organization. In this study, we update our routines previously based on IPC for CPC and use the occasion for rethinking various parameter choices. The new maps are significantly different from the previous ones, although this may not always be obvious on visual inspection. We provide nested maps online and a routine for generating portfolio overlays on the maps; a new tool is provided for “difference maps” between patent portfolios of organizations or firms. This is illustrated by comparing the portfolios of patents granted to two competing firms—Novartis and MSD—in 2016. Furthermore, the data is organized for the purpose of statistical analysis.

Technologies are created through the collective efforts of individual inventors. Understanding inventors' behaviors may thus enable predicting invention, guiding design efforts or improving technology policy. We examined data from 2.8 million inventors' 3.9 million patents and found that most patents are created by 'explorers': inventors who move between different technology domains during their careers. We mapped the space of latent relatedness between technology domains and found explorers were 250 times more likely to enter technology domains that were highly related to the domains of their previous patents, compared to an unrelated domain. The great regularity of inventors' behavior enabled accurate prediction of individual inventors' future movements: a model trained on just 5 years of data predicted inventors' explorations 30 years later with a log-loss below 0.01. Inventors entering their most related domains were associated with patenting up to 40% more in the new domain, but with reduced citations per patent. These findings may be instructive for inventors exploring design directions, and useful for organizations or governments in forecasting or directing technological change.