1. Tracking Technical Emergence:
Can we predict the future?
NILS NEWMAN
NEWMAN@SEARCHTECH.COM
SEARCH TECHNOLOGY

2. Why the Future?
 Identifying who, what, when and where
in scientific, technical and patent
information is now a relatively well
understood process.
 Given current technical and
computational advances, can we go
beyond benchmarking and turn our
attention to forecasting the future?

3. What could we actually forecast?
 There are several facets of future events that would be interesting.
 Could we forecast:
 The next “aha!” content and moment?
 Invention?
 Innovation?
 Probably not. Not effectively and at least not directly.

4. So what can we forecast?
Technical Emergence

5. What is Technical Emergence
Technical Emergence is
a concept that has
attributes of:
 Novelty
 Persistence
 Community
 Growth

6. For Example: The Quantum Dot
The history of quantum dot
 Aha! in 1981
 Invention in 1986
 Innovation -- 2019?
But Quantum Dot has trackable
 Novelty
 Persistence
 Community
 Growth

7. The four dimensions
To be emergent, a concept must have all
four attributes.
All four attributes exist as traits in the
scientific, technical, and patent literature.
All four traits can be measured using
bibliometric and ‘tech mining’ techniques.
This combination means we might have a
chance to do effective forecasting.

8. The forecast
Unfortunately, each attribute
presents unique forecasting
challenges.

9. Novelty
 One cannot really predict the appearance of a concept that does not yet
exist; but one can analyze the past rate at which new concepts have
emerged within a specified technical area.
 One can track and forecast progressions of incremental change.
 One can also use past activity to determine a probability for future
radical change, but with a higher degree of uncertainty.

10. Persistence
 Persistence is easy to measure and allows for effective forecasts.
 However, there is still a great deal of uncertainty as to how persistent a
concept must be to be considered persistent.
 The scale of the concept and the technical area both impact the
behavior of persistence.
 Persistence is also a great source of noise in forecasting process

11. Community
 Community can be difficult to measure.
 Complete and accurate information on all documents’ contributors is
not always available.
 Web of Science/Scopus work reasonably well. EI Compendex/INSPEC
are more problematic. Patents vary, depending on authority.
 Cleaning of organizational and personal names, and accurate matching
of people with organizations, is a challenge.
 Once cleaned, the social network analysis needed to measure and
forecast community is reasonably well understood.
 However, there is still debate on the level of analysis to apply when
looking to ascertain “a community.”

12. Growth
 Growth is tricky in that it comprises
multiple dimensions:
 Growth within the concept’s technology
space.
 Growth into other technology spaces.
 Growth within a community.
 Once identified, there are a variety of
ways to forecast future growth.
 One technique involves curve fitting to
logistics curves.

13. Sounds difficult….
 So what can we actually do?
 What would we do with it?

14. To simplify, let’s start with what to do
with it
 One of the best places to apply
this kind of forecasting process is
in Open Innovation (OI).
 An effective forecast of
emergence can yield new ideas
within a technology space and,
more importantly, find new
partners for your organization.

15. So let’s focus on OI partners
 The emergence analysis would start with a
broad search of a general technology area
of interest to your organization.
 The search would cover patents,
publications, and any other relevant
information sources.
 The search would include a back file of at
least ten years.

16. The Emergence Calculations
 The emergence calculations would use
part of the data set as a benchmark file to
determine normal behavior.
 The most recent records would be
analyzed for novelty, persistence,
community, and growth.
 This analysis would provide a set of likely
emergent concepts.

17. Pivoting to People
 The current state of emergence research means that a list of terms will be
more than a bit noisy.
 The current processes have a weakness due to boundary issues:
• What is the most effective level of analysis?
• What is the ideal community size?
• What is the boundary between persistent and old?
• What growth rate is optimal?
 These issues introduce false positives into concept lists.
 One way around this issue is to focus on organizations more than on terms.
 Working hypothesis: Organizations with a higher concentration of emergent
concepts are better candidate partners than organizations with a lower
concentration.

18. The output
 The output of an emergence analysis
would be:
 A list of organizations and researchers who
are operating in your area of interest.
 These people and organizations show
activity in cutting edges of your target
space.
 They could be useful resources for
your Open Innovation efforts.

19. Back to Quantum Dots…
 Running this kind of analysis on
Quantum Dots yields roughly
30,000 phrases in around 4,000
patent abstracts, of which nearly
100 terms qualify as emergent
concepts.
 As expected, the organizations
which strongly use emergent
terminology are primarily
University and Government labs
and are mostly located in China.
 The exercise produces a quick list
of organizations on which to focus
a human-centric OI vetting
process.

20. Summary
 Analyzing Technical Emergence appears to be a
promising approach to forecast technical
activity.
 Currently the approach is more effective at
targeting organizations and people. Specific
topics can be extracted, but false positives are
still a significant issue.
 As research in this area increases, precision and
recall of Emergence will improve.
 Understanding of Technical Emergence could
potentially have significant impacts on Open
Innovation and research strategies in general.

21. Discussion

22. Thank You! Merci Beaucoup!