Evidence from patents and papers
Where do new ideas and technologies come from? One school of thought says they are born from novel combinations of pre-existing ideas. To some extent that’s true by assumption, since everything can be decomposed into a collection of parts. But this school of thought makes stronger claims. One such claim is that new combinations - those pulling together disparate ideas - should be particularly important in the history of ideas.
And it turns out we have some pretty good evidence of that, at least from the realms of patents and academic papers (and also computer programming). To get at the notion that new ideas are combinations of older ideas, these papers all need some kind of proxy for the pre-existing ideas that are out there, waiting to be stitched together. With one exception, they all ultimately rely on classification systems that either put papers in different journals, or assign patents to different technology categories. These journals or technology classifications are then used as stand-ins for different ideas that can be combined. A paper that cites articles from a monetary policy journal and an international trade journal would be assumed to be combining ideas from these disciplines then. Or a patent classified as both a “rocket” and “monorail” technology would be assumed to combine both ideas into a new package technology.
A classic paper here is Fleming (2001), which uses highly specific patent subclasses to proxy for combining technologies. There were more than 100,000 technology subclasses at the time of the paper’s analysis, each corresponding to a relatively narrow technological concept. Using a sample of ~17,000 patents granted in May and June 1990 Fleming calculates the number of prior patents assigned the exact same set of subclasses. He shows patents assigned combinations without much precedent tend to receive more citations, which suggests patents that combined rarely combined concepts were indeed more important. For example, as we go from a patent assigned a completely original set of subclasses to a patent with the maximum number of prior patents assigned the same set of subclasses, citations fall off by 62%.
This flavor of result holds up pretty well to a variety of differing methods. For example, Arts and Veugelers (2015) track new combinations in a slightly different way than Fleming, and use a different slice of the data. Rather than counting the number of prior patents assigned the exact same set of technology classifications, they look at the share of pairs of subclasses assigned to a patent that have never been previously combined. This differs a bit from Fleming because they are only interested in patents that are the first to be assigned two disparate technology subclasses, and also because a patent might be a new combination and still be assigned no new pairs. For example, given subclasses A, B, and C, if the pairs AB, BC, and AC have each been combined before, but the set of all three (ABC) has not, then Fleming will code a patent assigned ABC as highly novel and Arts and Veuglers will not.
Arts and Veugelers (2015) look at ~84,000 US biotechnology patents granted between 1976 and 2001 and look at the citations received within the next five years. About 2.2% of patents that forge a new connection between different technology subclasses go on to be one of the most highly cited biomedical patents of all time, compared to just 0.9% of patents that fail to forge new connections. And patents that don’t become these breakthroughs still get more citations if they forge novel links between technology subclasses. Moreover, the direction of this relationship is robust to lots of additional control variables.
As a final example, He and Luo (2017) also establish this result, measuring novel combinations in yet another way, and using an even broader set of data. He and Luo look at ~600,000 US patents granted in the 1990s, and which contain 5 or more citations to other patents. Rather than relying on the technology classifications assigned directly to these patents, they look at the classifications assigned to cited references. They assume a patent combines ideas from the classifications of the patents it cites. They also use a much coarser technology classification system, which has just 630 different technology categories, rather than over 100,000 used in the previous two papers. To measure novel combinations, they look at how frequently a pair of technology classifications are cited together relative to what would be expected by chance. That means they end up with lots of measures of novelty for each patent, one for every possible pair of cited references. To collapse down the set of novelty measures for each patent, they order the pairs of cited reference from the least conventional to most and then grab the median and the 5th percentile.
As a measure of the importance of these patents, we can look at the probability that they are a highly cited patent for the year they were granted and for their technology class. In the figure below, they divide patents up into deciles and compute the probability a patent whose novelty measure falls into that decile is a hit patent. Because they are adapting some earlier work, they set these indices up in a kind of confusing way. In the left figure below, moving from left to right we get increasingly conventional patents, while in the right figure, moving from left to right we get increasingly more unconventional patents.
The figure above shows that when you focus on the most unusual combination of cited technologies made by a patent (the right figure), then more atypical patents have a significantly higher chance of being a hit patent.
When you focus on the median, you find a more complicated relationship: you don’t want all the combinations made to be totally conventional nor totally unconventional and strange. There’s a sweet spot in the middle. Perhaps patents that are completely stuffed with weird combinations are too weird for future inventors to understand and build on?
The link between unusual combinations of technology classifications and future citations received is pretty reliable across these papers. But before taking these results too far, there are a few potential issues we need to look into.
The first potential issue is a form of selection bias. One challenge from this literature is we typically only ever look at patents that are ultimately granted. But suppose patent examiners are biased against patent applications that make unusual combinations. If that’s the case, then patents making unusual combinations will only make it through if they are so valuable that their merits overcome this deficit. That would, in turn, mean when we are comparing patents that make unusual combinations to other patents, we are comparing a set of elite patents (those good enough to clear the application hurdle) to average patents.
Why would examiners be biased against patents making unusual combinations? This could be the case, for example, if evaluating such a patent requires the examiner to draw on ideas from different disciplines and this is hard to do when the disciplines are particularly unfamiliar and unusual.
Ferguson and Carnabuci (2017) look into this issue using data from the European Patent Office (EPO), which has better data on patent applications that are ultimately not granted, but which otherwise has similar patent application processes as the US Patent and Trademark Office. Before getting into their results on whether new combinations are less likely to be approved, it is worth pointing out that, again, they replicate the same results as the preceding papers: patents at the EPO that make less common combinations tend to receive more citations.
When Ferguson and Carnabuci look at applications, rather than granted patents, they reassuringly find that patent applications that make more unusual combinations are no less likely to be granted. In fact, the opposite is true - highly conventional patents are actually a bit less likely to be approved than unconventional patents! However, this effect is weak enough that when Ferguson and Carnabuci correct for it, the impact on their results is minor. In other words, our first concern is unfounded.
(Ferguson and Carnabuci do find other sources of bias at the patent office though. Patents that draw on more technological disciplines do face a tougher time getting a grant, and this does lead to a spurious inflation of the citations received by patents drawing on many different technological disciplines, since only very good patents overcome this bias. Somewhat surprisingly though, it turns out that drawing on many technological disciplines is relatively uncorrelated with the conventionality of these combinations, which is why we do not detect any significant effect of unconventional combination on the probability of getting approved.)
On to a second possible criticism of this literature. So far, all these papers have looked at the “importance” of a patent by looking at how many citations it receives. We might be interested in a more direct measure of the influence of combinations on the inventive process.
In Clancy (2018) (yes, that’s me), I used yet another technology classification system, this one consisting of the 13,000+ “mainline subclasses” in the US patent classification system, as proxies for the underlying technological “components” of patented innovations. For example, a mainline sub-class might be “Bridge; truss” or “Data processing (artificial intelligence); neural network.” When a patent gets assigned more than one of these, I interpret it as an invention combining these technological components, as in Fleming (2001) or Arts and Veuglers (2014).
Clancy (2018) has a slightly different take on combinatorial innovation than the preceding papers. Instead of just looking at the citations received by a patent making unusual combinations, it tries to get directly at the state of “combinatorial knowledge” so to speak, and see how it influences which patents subsequently get invented. One nice thing about the combinatorial perspective on innovation, is that if you know all the possible technological building blocks you can exhaustively enumerate all possible inventions, including ones that are never actually invented. For example, once components A, B, and C, exist, then the combination ABC is a possible invention, whether or not anyone has actually “done” it. Then you can see what kinds of factors are correlated with possible inventions being realized.
I show a patent with a given combination ABC is more likely to be filed in a year when there are more (prior) patents combining any of the pairs AB, AC, and BC, but less likely to be filed when there are more (prior) patents combining all three (ABC). The following two figures illustrate these dynamics (the different lines correspond to different values for other explanatory variables; i.e., all other variables take the value of the mean, or the mean +/- a standard deviation).
In each figure, we plot the expected annual number of patent applications as we vary the number of times the constituent pairs have been used (above) or the number of times the entire triple-combination has been used (below).
The idea is that inventors learn from the example of successful patents, so anytime they see an invention successfully combining A and B, A and C, or B and C, they have more information about how to combine A, B, and C. Accordingly, it’s more likely such patents will be filed, as in the first figure. On the other hand, patents combining A, B, and C are also instructive, but they also make it harder to patent since it becomes harder to make a novel (patentable) contribution, as in the second figure.
The implication is that innovation enables neighboring ideas that draw on similar but not exactly the same components (another example of how it’s often ideas that are neither too close nor too far that are most useful). It also suggests a reason why so many other papers find patents making unusual combinations receive more citations. In Clancy (2018), new combinations generate valuable knowledge that is used to create similar patents. These are probably more likely to cite their antecedents. Moreover, like Fleming (2001), I find that when a given combination of subclasses becomes more common, patents with the exact same set of subclasses become less likely, which probably attenuates future citations to them. In short, Clancy (2018) suggests citations may well reflect actual new invention that is enabled by unusual combinations of patent.
As mentioned at the outset, we can apply similar ideas to academic papers: do the best new ideas reflect combinations of old ideas here too?
Carayol, Lahatte, and Llopis (2019) investigate by using the keywords that authors attach to their own manuscripts as proxies for the ideas that are being combined. For a dataset of about 10 million papers published between 1999 and 2013, they look at each pair of keywords used in each paper, comparing how many other papers use the same pair of keywords as compared to what would be expected if keywords were just assigned randomly and independently. Using this metric of novelty, they find the more novel the paper, the more citations it gets and the more likely it is to be among the top 5% most cited. In the figures below, papers are sorted into 100 bins from least novel (left) to most novel (right), and the average citations received within 3 years or the probability of being among the top 5% most cited papers for papers in the same centile is on the vertical axis.
Wang, Veugelers, and Stephan (2017) get similar results with a different proxy for the ideas being combined. They define highly novel papers as those whose citations “combine” journals not previously connected (weighted by how distant the journals are, in terms of the overlap of journals they are usually cited with). They find all sorts of interesting things about papers that make these novel connections. As with Fleming, they find these papers tend to be more highly cited in the long run.
However, they also find evidence of bias against novelty in science: papers making highly novel connections are less likely to appear in high-impact journals, receive a higher share of citations from outside their own field, and citations they do receive take longer to arrive. This suggests there may be some forces of conservatism in science - it would be interesting to know if similar biases show up in the citation data of patents.
Uzzi, Mukherjee, Stringer, and Jones (2013) also looks at papers that make atypical connections between cited journals. Like He and Luo (2017) (which actually adopts their methodology and applies it to patents), they measure the novelty of a combination of journals by the number of times journals have been cited together relative to what would be expected by chance. They also find some ambivalent results about about novelty. The papers most likely to become “hits” (top 5% most highly cited) are those that make a small number of atypical connections but are otherwise highly conventional. Papers that are novel across the board (“low median convention” and “high tail novelty” in the figure below) don’t fare as well.
All three of these papers find academic papers that make at least some unusual combination receive more citations, consistent with the evidence from patents. Though, again suggesting the possibility of greater conservatism in science, Uzzi and coauthors find for papers it’s good to be highly conventional, whereas He and Luo find an intermediate value of “conventionality” is best for patents (see the first figure in this post, left).
The bottom line though is that a number of papers drawing on different slices of patent data or academic papers find the same thing: innovation that pulls together disparate pre-existing ideas tends to be a particularly useful. The schools of thought that emphasizes combination as the heart of innovation are probably on to something.
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Fleming, Lee. 2001. Recombinant Uncertainty in Technological Space. Management Science 47(1): 117-132. https://doi.org/10.1287/mnsc.18.104.22.16871
Arts, Sam, and Reinhilde Veugelers. 2015. Technology familiarity, recombinant novelty, and breakthrough invention. Industrial and Corporate Change, 24(6): 1215–1246. https://doi.org/10.1093/icc/dtu029
He, Yuejun, and Jianxi Luo. 2017. Novelty, Conventionality, and Value of Invention. Design Computing and Cognition ‘16, ed. J.S. Gero. Springer International Publishing, Switzerland: 23-38. https://doi.org/10.1007/978-3-319-44989-0_2
Ferguson, John-Paul, and Gianluca Carnabuci. 2017. Risky Recombinations: Institutional Gatekeeping in the Innovation Process. Organization Science 28(1). https://doi.org/10.1287/orsc.2016.1106
Clancy, Matthew S. 2018. Inventing by Combining Pre-Existing Technologies: Patent Evidence on Learning and Fishing Out. Research Policy 47(1): 252-265. https://doi.org/10.1016/j.respol.2017.10.015
Carayol, Nicolas, Agenor Lahatte, and Oscar Llopis. 2019. The Right Job and the Job Right: Novelty, Impact and Journal Stratification in Science. SSRN working paper. http://dx.doi.org/10.2139/ssrn.3347326
Wang, Jian, Reinhilde Veugelers, and Paula Stephan. 2017. Bias against novelty in science: A cautionary tale for users of bibliometric indicators. Research Policy 46(8): 1416-1436. https://doi.org/10.1016/j.respol.2017.06.006
Uzzi, Brian, Satyam Mukherjee, Michael Stringer, and Ben Jones. 2013. Atypical Combinations and Scientific Impact. Science 342(6157): 468-472. https://doi.org/10.1126/science.1240474