Fig 4 - uploaded by Sergi Valverde
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Evolution of video camera technology. Top: Time series of keyword (lens, camera, digital, and video) frequencies in a large collection of technical books. Middle: Significant inventions include: (A) Petzval lens (1840) (B) Video camera with detachable magnetic tape (1974) (C) Electronic still camera with digital data storage (1977) and (D) video camera with LCD view and controls (1989). Bottom: Time evolution of a network motif representing semantic relations between video camera concepts.
Source publication
Can we use the framework of evolution to understand technological innovation? This question has been explored in the past using different approaches. Here, we propose that the convergence of computer science, evolutionary biology and statistical physics is a promising approach to study technological evolution. Using this novel framework, we show th...
Context in source publication
Context 1
... example, we can explore collections of technical documents at different scales. An example is shown in figure 4, which shows the evolution of video camera technology at three different levels. The problem of photographic lens design was addressed well before the invention of the first cameras in the early 19th century. ...
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Background
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Citations
... Patent citations form networks (Breschi and Lissoni 2004). Those networks of patent citations have been investigated by a rich literature (Érdi et al. 2013;Valverde 2014;Valverde et al. 2007). Those investigations evaluate properties of those networks, revealing their growth (Valverde 2014, Figure 2), their frequency distribution over time (Strandburg et al. 2009(Strandburg et al. , 1669, their stratification (Strandburg et al. 2009(Strandburg et al. , 1670, the power law property of those distributions (Valverde 2014, 3), characteristics of specific networks for different products (Valverde 2014) and use of those networks to predict emerging technologies in specific patent classes (Érdi et al. 2013). ...
... Those networks of patent citations have been investigated by a rich literature (Érdi et al. 2013;Valverde 2014;Valverde et al. 2007). Those investigations evaluate properties of those networks, revealing their growth (Valverde 2014, Figure 2), their frequency distribution over time (Strandburg et al. 2009(Strandburg et al. , 1669, their stratification (Strandburg et al. 2009(Strandburg et al. , 1670, the power law property of those distributions (Valverde 2014, 3), characteristics of specific networks for different products (Valverde 2014) and use of those networks to predict emerging technologies in specific patent classes (Érdi et al. 2013). ...
... Those networks of patent citations have been investigated by a rich literature (Érdi et al. 2013;Valverde 2014;Valverde et al. 2007). Those investigations evaluate properties of those networks, revealing their growth (Valverde 2014, Figure 2), their frequency distribution over time (Strandburg et al. 2009(Strandburg et al. , 1669, their stratification (Strandburg et al. 2009(Strandburg et al. , 1670, the power law property of those distributions (Valverde 2014, 3), characteristics of specific networks for different products (Valverde 2014) and use of those networks to predict emerging technologies in specific patent classes (Érdi et al. 2013). ...
This paper presents a database with USPTO patents for selected years between 1991 and 2009, totaling 1,022,490 patents, 786,780 patents with international citations and 4,064,995 cross-border citations - links in our analysis. We evaluate a network from those international links, with nodes that are institutions - patent assignees. The literature review organizes arguments for patent citations as knowledge flows and acknowledges problems such as differences between applicants and examiners citations - an exercise to deal with this problem is presented. This network has firms as the dominant institution. An inter-temporal analysis shows the network growth over time and the preservation of its scale-free structure, evidence of its resilience. Over time, this network evolves, changing the leading sectors in a matrix of interaction between citing and cited patents - indications of changes caused by the emergence of new sectors.
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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.
