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Commercializing legal AI
research: lessons learned
Anna Ronkainen
Chief Scientist, TrademarkNow Inc.
@ronkaine
Where I’m coming from
• worked 1995–2009 at a company started by
two U of Helsinki professors to commercialize
their work in computational linguistics/NLP
• co-founded TrademarkNow in 2012 to build
tools for trademark law based on my own
research in computational legal theory
• executive committee member in Int’l Assoc for
AI and Law (IAAIL) since January; portfolio:
industry liaison
SOME GOOD EXAMPLES
Lex Machina
• started as a research project (IP Litigation
Clearinghouse) between Stanford Law and CS
departments
• spun off as an independent company in 2008
• initial university funding $3M, seed $2M,
series A $4.8M (according to Wikipedia)
• sold to LexisNexis in 2015
TrademarkNow
• started(1) as a research project into
computational modelling of vague legal concepts,
using trademark likelihood of confusion as a
random example
• started(2) as one frustrated trademark lawyer’s
quest for better tools
• incorporated in 2012, 4 co-founders (3 tech, 2
legal)
• funding: $300k seed, $3M series A, plus gov’t
innovation funding (Tekes)
Riverview Law
• one of the biggest ABSs in the UK
• started a collaboration with University of
Liverpool in January 2015
• Riverview pays for two(?) researchers to work
on figuring out how to use the legal AI
research from Liverpool (esp. strong on legal
argumentation) to solve business problems
• success? I guess too early to tell yet
Dutch government agencies
• e.g. Tax and Customs Administration and
Immigration and Naturalization Service (IND)
• leveraging the strong Dutch AI & law
community by using university researchers in
consulting roles
• development work done by the usual external
suppliers
• e.g. INDIGO: http://vimeo.com/43187024
LESSONS LEARNED
Why research commercialization fails
• most such ventures fail for a simple reason: putting the
cart before the horse
• solution looking for a problem, not the other way
around
• academics (typically) don’t have a very commercially
oriented mindset
• perhaps most importantly, product design and
management are often left out of the equation
altogether
• basic research is a fairly blunt instrument: research
end-product (good enough for publication) very
different from a marketable and commercially viable
product
What academics can do about it
• consider potential uses even when planning
and carrying out basic research
• and of course there’s also applied research:
for legal tech, a lot of general AI/NLP stuff just
waiting to be (tried out to see if it can be)
used (cf. e-discovery)
• try to take an active role in seeking out
potential partners for commercialization (no
time for that, I know...)
Applied and basic research:
Pasteur’s quadrant
Quest for
fundamental
understanding? yes
Pure basic
research
(Bohr)
Use-inspired
basic research
(Pasteur)
no
-
Pure applied
research
(Edison)
no yes
Considerations of use?
(Stokes 1997)
The other part of the equation:
The people with the actual problems
• you are more likely to end up with a viable
product when you start with a problem and
use research to look for a solution, not the
other way around
• the initiative should come from someone who
has experienced the pain points first hand – or
at least people who can see an inefficiency,
have an idea about what to do about it, and
can figure out how to fill in the blanks
Thank You
@ronkaine @TrademarkNow
https://www.trademarknow.com/
http://iaail.org/

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Commercializing legal AI research: lessons learned

  • 1. Commercializing legal AI research: lessons learned Anna Ronkainen Chief Scientist, TrademarkNow Inc. @ronkaine
  • 2. Where I’m coming from • worked 1995–2009 at a company started by two U of Helsinki professors to commercialize their work in computational linguistics/NLP • co-founded TrademarkNow in 2012 to build tools for trademark law based on my own research in computational legal theory • executive committee member in Int’l Assoc for AI and Law (IAAIL) since January; portfolio: industry liaison
  • 4. Lex Machina • started as a research project (IP Litigation Clearinghouse) between Stanford Law and CS departments • spun off as an independent company in 2008 • initial university funding $3M, seed $2M, series A $4.8M (according to Wikipedia) • sold to LexisNexis in 2015
  • 5. TrademarkNow • started(1) as a research project into computational modelling of vague legal concepts, using trademark likelihood of confusion as a random example • started(2) as one frustrated trademark lawyer’s quest for better tools • incorporated in 2012, 4 co-founders (3 tech, 2 legal) • funding: $300k seed, $3M series A, plus gov’t innovation funding (Tekes)
  • 6. Riverview Law • one of the biggest ABSs in the UK • started a collaboration with University of Liverpool in January 2015 • Riverview pays for two(?) researchers to work on figuring out how to use the legal AI research from Liverpool (esp. strong on legal argumentation) to solve business problems • success? I guess too early to tell yet
  • 7. Dutch government agencies • e.g. Tax and Customs Administration and Immigration and Naturalization Service (IND) • leveraging the strong Dutch AI & law community by using university researchers in consulting roles • development work done by the usual external suppliers • e.g. INDIGO: http://vimeo.com/43187024
  • 9. Why research commercialization fails • most such ventures fail for a simple reason: putting the cart before the horse • solution looking for a problem, not the other way around • academics (typically) don’t have a very commercially oriented mindset • perhaps most importantly, product design and management are often left out of the equation altogether • basic research is a fairly blunt instrument: research end-product (good enough for publication) very different from a marketable and commercially viable product
  • 10. What academics can do about it • consider potential uses even when planning and carrying out basic research • and of course there’s also applied research: for legal tech, a lot of general AI/NLP stuff just waiting to be (tried out to see if it can be) used (cf. e-discovery) • try to take an active role in seeking out potential partners for commercialization (no time for that, I know...)
  • 11. Applied and basic research: Pasteur’s quadrant Quest for fundamental understanding? yes Pure basic research (Bohr) Use-inspired basic research (Pasteur) no - Pure applied research (Edison) no yes Considerations of use? (Stokes 1997)
  • 12. The other part of the equation: The people with the actual problems • you are more likely to end up with a viable product when you start with a problem and use research to look for a solution, not the other way around • the initiative should come from someone who has experienced the pain points first hand – or at least people who can see an inefficiency, have an idea about what to do about it, and can figure out how to fill in the blanks