The Emerging Clusters Model: A tool for identifying emerging technologies across multiple patent systems

doi:10.1016/j.respol.2014.06.006

Research Policy

Volume 44, Issue 1, February 2015, Pages 195-205

https://doi.org/10.1016/j.respol.2014.06.006 Get rights and content

Highlights

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The Emerging Clusters Model for identifying emerging technologies is proposed.
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The model locates emerging technologies in close to real time, not retrospectively.
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The model uses advanced forms of patent citation analysis.
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Patents identified by the model are significantly more highly cited than peer patents.

Abstract

Emerging technologies are of great interest to a wide range of stakeholders, but identifying such technologies is often problematic, especially given the overwhelming amount of information available to analysts and researchers on many subjects. This paper describes the Emerging Clusters Model, which uses advanced patent citation techniques to locate emerging technologies in close to real time, rather than retrospectively. The model covers multiple patent systems, and is designed to be extensible to additional systems. This paper also describes the first large scale test of the Emerging Clusters Model. This test reveals that patents in emerging clusters consistently have a significantly higher impact on subsequent technological developments than patents outside these clusters. Given that these emerging clusters are defined as soon as a given time period ends, without the aid of any forward-looking information, this suggests that the Emerging Clusters Model may be a useful tool for identifying interesting new technologies as they emerge.

Introduction

This paper discusses a model designed to help support the efforts of researchers and analysts attempting to locate emerging technologies. This model, named the Emerging Clusters Model, is specifically designed to locate emerging, high-impact technologies in close to real time, rather than retrospectively. That is, it attempts to identify what is emerging, not what has emerged. The Emerging Clusters Model is based on advanced patent citation techniques, developed to overcome the time lags associated with traditional approaches to patent citation analysis.

Earlier generations of the Emerging Clusters Model have been discussed in previous papers (Thomas and Breitzman, 2006, Chang and Breitzman, 2009). Those earlier generations of the model were largely exploratory, and covered limited patent systems and time periods. The current generation of the model, discussed in this paper, is much more ambitious, and covers multiple patent systems over an extended time period. This generation of the model is also much more flexible, and accounts for differences in referencing practices across patent systems, and changes in these practices over time. As a result, the model described in this paper is specifically designed to be extensible, both in terms of patent systems covered, and time periods examined.

This paper also describes the first large-scale, longitudinal test of the Emerging Clusters Model. The papers describing earlier generations of the model report promising results from individual years, but point to the need for a more general test of the model covering multiple years. This paper describes such a test, covering results from Emerging Clusters selected each year between 1980 and 2006 (the most recent year for which sufficient data are available to track the impact of patents in these clusters, as discussed later).

Section snippets

Background

In the study of innovation, a great deal of attention is paid to emerging technologies. Such technologies have the potential to be highly generative, and may open up whole new areas of technology and science. This potential often draws interest from various organizations. These include government agencies looking to fund promising new ideas, corporations hoping to gain a foothold in rapidly emerging fields, and investment institutions seeking returns from early investments in key innovators.

Overview of model

The Emerging Clusters Model uses an advanced form of patent citation analysis, a widely used technique for assessing the impact of patents on subsequent technological developments. The basic idea behind patent citation analysis is that highly cited patents (i.e. patents cited as prior art by many later patents) tend to contain technological information of particular importance. As such, they form the basis for many new innovations, and so are cited frequently by later patents. This does not

Data

The current generation of the Emerging Clusters Model, described in this paper, employs a much more comprehensive data set than did earlier generations of the model. Those earlier generations were exploratory in nature, and covered only two individual years. The current model, meanwhile, covers the period 1980–2011, with emerging clusters defined for each of those years, a total of thirty-two separate sets of emerging clusters. It should be noted that these are citing years. The underlying hot

Detailed description of model

This section of the paper describes in more detail the three steps involved in the Emerging Clusters Model. These steps are: identification of hot patents for a given time period; identification and clustering of next generation patents that cite hot patents during this time period; and scoring and ranking of next generation clusters based on characteristics of the patents contained within them. The output of this three-step process is a set of ranked clusters for a given time period, with the

Results

The output of the three-step process outlined in the previous section is a set of emerging clusters for a particular subject year. For example, there is a set of emerging clusters for 1980, a set of emerging clusters for 1981, and so on through 2011, the most recent year included in this analysis. The number of emerging clusters in each year is shown in Table 3. This table reveals that the number of clusters rose relatively steadily during the first two decades covered by the analysis,

Conclusions

This paper describes the Emerging Clusters Model, which is designed to identify interesting, emerging technologies in close to real time. The current version of the model covers patents from multiple systems, in contrast to the US focus of earlier generations of the model. This paper also discusses the first large-scale, longitudinal test of the model, in order to examine whether the promising results from earlier, limited data sets are also found using more comprehensive test data sets.

The

Conflict of interest

The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/NBC, DOD or the U.S. Government.

Acknowledgements

Supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center contract number D11PC20154; and by the US Department of Defense (DOD) via contract number HQ0034-12-C-0041. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

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The Emerging Clusters Model: A tool for identifying emerging technologies across multiple patent systems

Highlights

Abstract

Introduction

Section snippets

Background

Overview of model

Data

Detailed description of model

Results

Conclusions

Conflict of interest

Acknowledgements

Research Policy

Research Policy

Research Policy

World Patent Information

Technological Forecasting and Social Change

Expert Systems with Applications

Research Policy

Research Policy

Technological Forecasting and Social Change

Technological Forecasting and Social Change

Research Policy

Expert Systems with Applications

The Innovation Algorithm: TRIZ, Systematic Innovation, and Technical Creativity

A new product growth model for consumer durables

Management Science

Statistics notes: the odds ratio

British Medical Journal

The many applications of patent analysis

Journal of Information Science

The Emerging Clusters Project Final Report

A roadmap to the Smartphone Patent Wars and FRAND licensing

CPI Antitrust Chronicle

Using patents prospectively to identify emerging, high-impact technological clusters

Research Evaluation