The objective of this paper is to illustrate that analysis methods applied to innovation networks in the form of patented inventions can be used advantageously for economic development purposes. The results of the analysis can be used to support a range of capabilities from building communities of practice, identifying regional innovation centers, and understanding community structures to achieve economic growth through innovation. In addition, it is an objective of this paper to highlight the role of Modeling and Simulation (M&S) in the advancement of industries pertinent to specific regional economies.
We use Intellectual Property (IP) as a data source due to its impact on the economy and job growth. A February 2013 report by the Brookings Institution indicates that, “inventions, embodied in patents, are a major driver of long-term regional economic performance, especially if the patents are of higher quality. In recent decades, patenting is associated with higher productivity growth, lower unemployment rates, and the creation of more publicly-traded companies. The effect of patents on growth is roughly equal to that of having a highly educated workforce. A low-patenting metro area could gain $4,300 more per worker over a decade’s time, if it became a high-patenting metro area.”
In light of the Brookings Institution’s findings, this paper presents the results of a network analysis of the states Michigan and Texas, in an attempt to visualize the role played by Modeling and Simulation (M&S) in those states. These states were selected because they represent regions where there is a growing interest in and use of the techniques of M&S. It should be noted that these types of analysis can be applied to a spectrum of industries as well as states beyond those listed above, so the selected states and specific industry is meant to be exemplary rather than limiting.
The innovation network, our term for a patent network, is itself a model of the creation and flow of knowledge within an organization, region, state, or industry (entity) based on their patent portfolios and the patterns of interaction between the inventors who generate those patents. The topological patterns that characterize these networks and the network metrics that are associated with the individual inventors can provide valuable insights into the nature of the inventions and the social characteristics of the inventors themselves as well as industry investment. Further, analysis of the patents themselves will produce a deeper level of understanding of the role played by M&S (in this particular study) in each organization as well as the potential for interaction between the organizations, whenever interactions have not already been established.
For this study, the data can be obtained by two methods. The first is by querying the United States patent database (USPTO) for all inventions falling into Modeling & Simulation class 703 (every patent is assigned one primary technical classification and is binned with other patents in the same technology area), the second approach is to search the abstracts of the inventions for the terms (“modeling” AND “simulation”). The first approach will return inventions in the M&S domain based on the patent examiners perspective, and the second approach will return inventions in the M&S domain based on the inventors’ perspective of M&S, due to their own use of the terminology. Since the purpose of this study is to understand the impact of patents on economic development, we choose to collect the data through the second approach, the approach in which the perspective is that of the inventor and organization within each of the regions selected.
Modeling and Simulation in Michigan
Below in Figures 1 & 2 respectively, we provide the M&S innovation network for the state of Michigan over the period 2000-2013 and the Innovation Genotype™.
What you first notice, and as expected, the use of M&S in the state of Michigan is focused on the automotive industry, with Ford Global Technologies and GM Global Technologies dominating the innovation networks.
According to the Michigan Economic Development Corporation (MEDC), which is the state’s marketing arm and lead advocate for business development, the automotive industry is one of Michigan’s primary growth industries. They state, “Michigan produces more cars and trucks than any other state. It’s number one in employment of industrial and mechanical engineers and is home to the headquarters of 61 of the top 100 automotive suppliers. Additionally, Michigan has a premier automotive education program.”
The Brookings Institute report identified Detroit and Ann Arbor as the two largest patent producing cities in Michigan. In Detroit, the top five highest patenting companies are in the automotive industry, and in Ann Arbor, five of the top six highest patenting companies are also in the automotive industry. The sixth highest patenting entity in Ann Arbor is the University of Michigan.
In the innovation network below, Figure 1, you will also note that one of the groups identified is the University of Michigan. As stated above, metro areas with many patents, also see a growth in education. Searches for ties between the University of Michigan and the automotive industry turn up several connections. The university offers an automotive engineering program and there is a University of Michigan associated Transportation Research Institute that involves M&S to predict and demonstrate driver behaviors and performance.
The companies in Michigan that patent the most, are also the most patenting companies in M&S. It would appear that these companies deem M&S an important part of business operations, and as a result has an impact on economic development.
In order for these companies to make the most of their efforts, they should consider optimizing their innovation networks internally, while looking for external collaboration opportunities.
The Innovation Genotype™ is a method of analyzing the dominant technical arts being practiced within the entity as a whole. The USPTO classifies the subject matter of patents through a spectrum of numbered technical areas that it uses to characterize an invention. We analogize these areas to genes. For example, the closest technical area in the USPTO to our conception of M&S is area 703, which expresses “data processing, structural design, models, simulations and emulations.
In Figure 2, above, you will see the Primary Technology Classifications, which we analogize with “genes”, as assigned by the USPTO, from left to right in descending order based on the number of inventions. We make the analogy between technology classifications and genes for this reason. In a basic sense, genes enable specific processes in the organism, and the technology classes are a method of grouping patents, and those patents enable capabilities within the organization.
If you recall, the method of data collection was a query of abstracts that contained the terms “modeling” AND “simulation”, so the dataset was based on the inventors perspective of the primary focus of the invention. As it turns out, the majority of inventions (40) in the dataset, were identified by the patent examiners as belonging to Primary Class 703: “Data processing, structural design, models, simulations and emulations”, the technology class most closely aligned to M&S. This is an important qualifier, telling us that the majority of the inventions were either advancements in M&S itself, or the inventions leveraged M&S techniques that if unavailable, would not allow the invention to occur. This suggests to us that M&S is an industry itself, and should be considered for economic development to advance the industry. Of the 40 inventions in Class 703, over half of them were assigned to both Ford Global Technologies and GM Global Technologies collectively.
When we look at the other “genes” of the Innovation Genotype™, it reveals an interesting picture. We have inventions here that pertain to various automotive functions including data processing of vehicles for navigation (class 701), electricity for motive power systems (class 318), metal founding (class 164), motor vehicles (class 180), internal combustion engines (class 123) and brakes (class 188). In addition to the MEDC statement that 61 of the top 100 automotive suppliers are located in Michigan, the Detroit Regional Chamber, is home to three hundred and seventy-five automotive R&D centers. With such large numbers of suppliers and R&D centers, M&S is essential to economic development by allowing those suppliers and R&D centers to work more efficiently with auto manufacturers.
Continuing to look at the Innovation Genotype™ we see other classes that one might not expect, but in hindsight might be just as important to the automotive industry and an indicator towards economic development in industries outside of automotive. Inventors in Michigan thought inventions involving M&S are also important to railway switching and signaling (class 246) which is vital to the transportation of raw materials into manufacturing sites and shipping products out. Another “gene” was automatic temperature and humidity regulation (class 236), which could be applied to a cars A/C system, or controlling environmental conditions in a manufacturing plant.
Whether it is the more likely outcome of the analysis like navigation, combustion engines, and braking, or the less obvious railway management or environmental control, we can see that M&S has a large impact on the automotive industry. Policies should be examined to increase patents and inventions, as well as collaborations between academia and industry in Michigan to expand Michigan’s economic growth.
The increasingly strong connection between regional patent activity and economic success, and that M&S has a significant impact on high tech industries that are essential to the economic success to the state of Michigan. M&S has shown to be a key enabling technology.
Modeling and Simulation in Texas
Below in Figures 3 & 4 respectively, we provide the M&S innovation network and Innovation Genotype™ for the state of Texas over the period of 2000-2013.
Like Michigan above, you will notice when examining the innovation network in Figure 3, that Texas has a dominant industry, the energy industry, though other industries including information technology and semiconductors are present.
Within the energy industry, the dominant innovation networks are Schlumberger Tech and ExxonMobil Upstream Research, which were the second and third largest patent producers in Houston in 2011. It should be noted though, that while Schlumberger Tech is the larger network, it is highly splintered and disconnected. Would Schlumberger and the state of Texas benefit from having all of these separate inventor groups working on M&S within the energy industry collaborate more effectively, generating more innovations and intellectual property which would help drive the economy even more?
Beyond the energy industry, Texas also has several large networks of inventors using M&S in the high-tech industry at organizations including Texas Instruments, Hewlett-Packard and National Instruments Corporation. All three organizations are leveraging M&S. While their traditional products are in similar domains requiring overlapping expertise and skills, they are not considered traditional competitors.
In the state of Texas, M&S has a strong showing in both the energy sector and the high-tech industries. This provides an opportunity between none competitive industries for collaboration in the area of M&S that would prove to be beneficial for both parties and economic development on a whole by driving innovation in this region.
In Figure 4, above, you will see the Primary Technology Classifications as assigned by the USPTO from left to right in descending order based on the number of inventions.
In Texas, the majority of inventions (236) in the dataset, were identified by the patent examiners as belonging to Primary Class 703: “Data processing, structural design, models, simulations and emulations”, the technology class most closely aligned to M&S. The much greater number of inventions falling into the M&S class in Texas compared with Michigan, suggests that the industries and organizations within Texas rely and/or incorporate M&S into their business methods more frequently. Of the companies identified in the innovation network, Schlumberger Tech had the most patents in class 703 with 54 patents, while ExxonMobil Upstream Research has 6 patents. From the high-tech sector, Texas Instruments had the most with 15 patents, and Hewlett-Packard and National Instruments Corporation with 6 patents and 5 patents respectively.
Several of the other “genes” identified in the Innovation Genotype™ include data processing for measuring and calibration (class 702), which can be applied to many different industries, but we also see inventions in wells (class 166) and boring and penetrating the earth (class 175), both involved in the energy industry and resource extraction.
There are also “genes” that likely come from the IT sector which include education and demonstration (class 434), data processing for software (class 717), and semiconductor device manufacturing (class 438).
Finally, it is evident that in Texas there are inventions involving the application of M&S that would be of value to just about any industry. The Innovation Genotype™ above finds inventions in areas including data processing for financial transactions (class 705), communications (class 367) and data processing for artificial intelligence (class 706). If not currently the case, Texas is in a good position to advance and export M&S technology further building economic development.
Summary and Conclusions
The increasingly strong connection between regional patent activity, technical universities, economic success and job growth has grown more prominent in recent years. Within the states of Michigan and Texas the most aggressive patenting organizations consistently produced advances in Modeling and Simulation as it pertains to their respective industries. Given the cross platform application that M&S offers, there exists opportunity for organizations and policy makers to craft symbiotic relationships between government, academia and industry that will provide benefit for all parties.
Innovation network analysis provides a source of information to identify opportunities for governments and industry within both a geographic region and also specific technical domains. The visualization and characterization of a region’s capacity for innovation provides insight to decision makers about the status of economic growth and future prospects to pursue.
Further, this analysis leverages intellectual property to forge opportunities instead of creating strategic fences between competitors. This potential for collaboration among non-competitive entities within a region would fuel economic development and growth.
Devin Markovits email@example.com
Dr. Morton Tavel firstname.lastname@example.org