SlideShare a Scribd company logo
How to do things with
AI & law research
Dutch Legal Tech Meetup, 2015-11-02
Anna Ronkainen
Chief Scientist, TrademarkNow Inc.
@ronkaine
A tale of two origin stories
‘Preliminary try-outs of decision machines
built according to various formal specifications
can be made in relation to selected
administrative or judicial tribunals. The
Supreme Court might be chosen for the
purpose.’
(Harold Lasswell 1955)
‘Can we “feed” into the computer that the judge’s
ulcer is getting worse, that he had fought earlier
in the morning with his wife, that the coffee was
cold, that the defence counsel is an apparent
moron, that the temporarily assigned associate
judge is unfamiliar with the law and besides
smokes obnoxious cigars, that the tailor’s bill was
outrageous etc. etc.?’
(Kaarle Makkonen 1968, translation ar)
”As we know, there are known knowns. There
are things we know we know. We also know
there are known unknowns, that is to say, we
know there are some things we do not know.
But there are also unknown unknowns, the
ones we don’t know we don’t know.”
– Donald Rumsfeld (2002)
(Un)known (un)knowns
known	
unknowns	
known	
knowns	
unknown	
unknowns	
??
(Un)known (un)knowns
known	
unknowns	
known	
knowns	
unknown	
unknowns	
unknown	
knowns
(Un)known (un)knowns
conscious	
ignorance	
conscious	
knowledge	
unconscious	
ignorance	
unconscious	
knowledge
Dual-process cognition
System 1
•  evolutionarily old
•  unconscious, preconscious
•  shared with animals
•  implicit knowledge
•  automatic
•  fast
•  parallel
•  high capacity
•  intuitive
•  contextualized
•  pragmatic
•  associative
•  independent of general
intelligence
System 2
•  evolutionarily recent
•  conscious
•  distinctively human
•  explicit knowledge
•  controlled
•  slow
•  sequential
•  low capacity
•  reflective
•  abstract
•  logical
•  rule-based
•  linked to general intelligence
(Frankish	&	Evans	2009)
Systems 1 and 2 in legal reasoning:
interaction
System 1:
making the
decision
System 2:
validation and
justification
(Ronkainen	2011)
What’s that got to do with legal AI?
-  MOSONG, my 1st (and so far only) system
prototype
-  built for studying the use of fuzzy logic in
modelling various issues in legal theory
-  specifically, the use of Type-2 fuzzy logic for
modelling vagueness and uncertainty
-  trademarks initially just a random example
domain
-  but the knowledge acquired through this
research also proved useful for TrademarkNow...
Open texture
‘Whichever device, precedent or legislation,
is chosen for the communication of
standards of behaviour, these, however
smoothly they work over the great mass of
ordinary cases, will, at some point where
their application is in question, prove
indeterminate; they will have what has
been termed an open texture.’
- (Hart 1961)
Standard example of open texture :
No vehicles in a park
‘When we are bold enough to frame some general
rule of conduct (e.g. a rule that no vehicle may be
taken into the park), the language used in this
context fixes necessary conditions which anything
must satisfy if it is to be within its scope, and
certain clear examples of what is certainly within its
scope may be present to our minds.’ (Hart 1961)
... but that’s really a stupid example because
vehicles are already categorized in excruciating
detail so being more precise costs nothing
Inescapable open texture:
No boozing in a park (but “civilized”
drinking is okay)
Section 4
Intake of intoxicating substances
The intake of intoxicating substances is prohibited in public
places in built-up areas [...].
The provisions of paragraph 1 do not concern [...] the intake
of alcoholic beverages in a park or in a comparable public
place in a manner such that the intake or the presence
associated with it does not obstruct or unreasonably
encumber other persons’ right to use the place for its
intended purpose.
(Finland: Public Order Act (612/2003))
Inescapable open texture:
Trademark similarity (Mosong)
Article 8
Relative grounds for refusal
1. Upon opposition by the proprietor of an earlier trade mark, the
trade mark applied for shall not be registered:
(a) if it is identical with the earlier trade mark and the goods or
services for which registration is applied for are identical with the
goods or services for which the earlier trade mark is protected;
(b) if because of its identity with or similarity to the earlier trade
mark and the identity or similarity of the goods or services
covered by the trade marks there exists a likelihood of confusion
on the part of the public in the territory in which the earlier trade
mark is protected; the likelihood of confusion includes the
likelihood of association with the earlier trade mark.
[...]
(CTM Regulation (40/94/EC))
Mosong: the domain
Tentative rule
Article 8
Relative grounds for refusal
1. Upon opposition by the proprietor of an earlier trade mark, the
trade mark applied for shall not be registered:
(a) if it is identical with the earlier trade mark and the goods or
services for which registration is applied for are identical with the
goods or services for which the earlier trade mark is protected;
(b) if because of its identity with or similarity to the earlier trade
mark and the identity or similarity of the goods or services
covered by the trade marks there exists a likelihood of confusion
on the part of the public in the territory in which the earlier trade
mark is protected; the likelihood of confusion includes the
likelihood of association with the earlier trade mark.
REFUSAL = MARKS-SIMILAR and GOODS-SIMILAR
‘Training’ set: 119 cases
“Training set”
119 OHIM cases from 1997–2000, of which
107 from the Opposition Division (1st instance)
and
12 from the Boards of Appeal (2nd instance)
Results for the training set
0
0.2
0.4
0.6
0.8
1
Validation set
30 most recent (2002) relevant cases from OHIM:
20 from the Opposition Division and
10 from the Boards of Appeal
Result*: all cases predicted correctly
* when coded into the system by a domain expert
Results for the validation set
0
0.2
0.4
0.6
0.8
1
Non-expert validation
•  done by non-law students taking a course on
•  intellectual property law (n=75)
•  original validation set in two parts (15+15 cases)
•  at the beginning and the end of the course
•  completed non-interactively through a web form
•  correct answer: 54.6±6.5%
•  incorrect answer: 25.9±7.5%
•  no answer: 19.5±5.2% (± = σ)
Non-expert validation
% ±stderr before after total
group 1 (n=15) 41.3±1.7 65.8±2.8 53.5±1.7
group 2 (n=12) 46.1±2.0 65.0±3.0 55.6±1.9
group 3 (n=48) 43.3±1.3 65.9±1.3 54.7±0.9
total (n=75) 43.4±1.0 65.8±1.1 54.6±0.8
Initial conclusions from this work
-  it (sort of) works; using fuzzy logic makes
sense in this context
-  poses more questions than it answers...
-  ...and that’s how I ended up trying to
reverse-engineer human lawyers rather than
just trying to build systems based on existing
legal theory literature
Implications for legal AI
-  using rule-based methods has its advantages
-  human-readable
-  comparatively quick to develop
-  modifiable (esp. relevant wrt legislative
changes)
-  but they can’t do the work alone
-  can’t make sense about situations which they
weren’t specifically built to handle
-  real-world complexity needs (sometimes)
statistical/machine-learning approaches
That second origin story...
It started with a frustrated trademark
attorney...
-  Mikael Kolehmainen – now TMnow CEO – worked as a
trademark attorney at one of the biggest boutique TM
firms in Finland
-  he was deeply frustrated with the existing way of doing
trademark searches and wanted to do something about it
-  first he found Matti Kokkola (CTO) through a common
acquintance
-  then me (Chief Scientist) via the university, totally by
accident
-  and finally Heikki Vesalainen (Chief Architect) who had co-
founded a company with Matti as sophomores
-  development work started spring 2012, seed funding from
Lifeline Ventures in August 2012
-  first release (then as Onomatics Quick Search) Oct 2012
So, what came out of it?
About TrademarkNow
-  trademark legal technology provider
founded in 2012, based in Helsinki, NYC
and Kilkenny, now ~30 employees
-  total funding so far ~3MEUR (plus gov’t
grants and loans (Tekes))
-  products based on a unique model of
likelihood of confusion for trademarks
-  NameCheck: intelligent TM search
-  NameWatch: intelligent TM watch
Trademark search: Ye olde way
-  TM lawyer formulates search strategy (wildcards
& classes to be used etc.)
-  paralegal carries out the search and hands the
results to the lawyer
-  lawyer browses through the results and marks
the ones for which more info needed
-  paralegal retrieves additional info
-  lawyer reviews said info, evaluates risk, reports
-  whole process typically takes several days,
stakeholders often expect results immediately
Trademark search: The new way
Trademark search: The new way
Not just trademark search
Core component: AI model of
likelihood of confusion (TM similarity)
-  similarity of trademarks
-  phonetical
-  graphic
-  semantic
-  currently only word marks
-  similarity of goods and services
-  others do this only using the 45 classes of
the Nice Classification
Mix-and-match approach to AI
techniques
-  traditionally AI has been divided into to
factions: rule-based and statistical
-  all our competitors are also either-or
-  both have their advantages and
disadvantages
-  unlike the mainstream, we’re flexible and
use both as we see fit, to maximize their
benefits
Customers ♥ TrademarkNow
Customers ♥ TrademarkNow
Lessons learned & stuff
Research commercialization is difficult
in general – not only for AI & law
-  innovation and commercialization are tossed
around as vital research policy goals a lot these
days pretty much wherever you go
-  said tossers* tend to treat it as a black box,
basically thinking that telling academics to be
innovative is all it takes
-  there are two parts in the equation, and only
one of them can be said to be the academics’
responsibility
* sorry, couldn’t resist
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
The first part of the equation:
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
Een kans uitzonderlijk voor Nederland
-  veel geld geïnvesteerd in rechtsinformatisch
wetenschappelijk onderzoek in de jaren 1980
-  de gerichte investering nu weg maar de sporen
zijn nog duidelijk te zien in het AI & law-milieu
(bijv. JURIX)
-  meerdere succesvolle projecten in
samenwerking met de overheid
-  maar (bijna?) geen startups met oorsprong in
het onderzoeksmilieu
-  dus potentieel een enorme bron van expertise
voor nieuwe juridische startups te gebruiken
Dank U!

More Related Content

What's hot

Introduction to Legal Technology, lecture 1 (2015)
Introduction to Legal Technology, lecture 1 (2015)Introduction to Legal Technology, lecture 1 (2015)
Introduction to Legal Technology, lecture 1 (2015)
Anna Ronkainen
 
What is legal technology?
What is legal technology?What is legal technology?
What is legal technology?
Anna Ronkainen
 
Introduction to Legal Technology, lecture 10 (2015)
Introduction to Legal Technology, lecture 10 (2015)Introduction to Legal Technology, lecture 10 (2015)
Introduction to Legal Technology, lecture 10 (2015)
Anna Ronkainen
 
Modeling meaning and knowledge: legal knowledge
Modeling meaning and knowledge: legal knowledgeModeling meaning and knowledge: legal knowledge
Modeling meaning and knowledge: legal knowledge
Anna Ronkainen
 
Finnish Legal Tech Forum launch presentation
Finnish Legal Tech Forum launch presentationFinnish Legal Tech Forum launch presentation
Finnish Legal Tech Forum launch presentation
Anna Ronkainen
 
Introduction to Legal Technology, lecture 5 (2015)
Introduction to Legal Technology, lecture 5 (2015)Introduction to Legal Technology, lecture 5 (2015)
Introduction to Legal Technology, lecture 5 (2015)
Anna Ronkainen
 
Introduction to Legal Technology, lecture 6 (2015)
Introduction to Legal Technology, lecture 6 (2015)Introduction to Legal Technology, lecture 6 (2015)
Introduction to Legal Technology, lecture 6 (2015)
Anna Ronkainen
 
Introduction to Legal Technology, lecture 9 (2015)
Introduction to Legal Technology, lecture 9 (2015)Introduction to Legal Technology, lecture 9 (2015)
Introduction to Legal Technology, lecture 9 (2015)
Anna Ronkainen
 
Commercializing legal AI research: lessons learned
Commercializing legal AI research: lessons learnedCommercializing legal AI research: lessons learned
Commercializing legal AI research: lessons learned
Anna Ronkainen
 
Helsinki Legal Tech Meetup introduction
Helsinki Legal Tech Meetup introductionHelsinki Legal Tech Meetup introduction
Helsinki Legal Tech Meetup introduction
Anna Ronkainen
 
Helsinki Legal Tech Meetup: TrademarkNow demo/presentation
Helsinki Legal Tech Meetup: TrademarkNow demo/presentationHelsinki Legal Tech Meetup: TrademarkNow demo/presentation
Helsinki Legal Tech Meetup: TrademarkNow demo/presentation
Anna Ronkainen
 
Ai and applications in the legal domain studium generale maastricht 20191101
Ai and applications in the legal domain studium generale maastricht 20191101Ai and applications in the legal domain studium generale maastricht 20191101
Ai and applications in the legal domain studium generale maastricht 20191101
jcscholtes
 
Legal tech Alliance Workshop 20191029
Legal tech Alliance Workshop 20191029Legal tech Alliance Workshop 20191029
Legal tech Alliance Workshop 20191029
jcscholtes
 
eDiscovery
eDiscoveryeDiscovery
eDiscovery
pabrown1219
 
A peek into open hardware: Motivations, Licensing, and Ecosystem
A peek into open hardware: Motivations, Licensing, and EcosystemA peek into open hardware: Motivations, Licensing, and Ecosystem
A peek into open hardware: Motivations, Licensing, and Ecosystem
Tomoaki Watanabe
 
HBS seminar 3/26/14: Dark Markets, Bad Patents, No Data
HBS seminar 3/26/14: Dark Markets, Bad Patents, No DataHBS seminar 3/26/14: Dark Markets, Bad Patents, No Data
HBS seminar 3/26/14: Dark Markets, Bad Patents, No Data
Brian Kahin
 

What's hot (16)

Introduction to Legal Technology, lecture 1 (2015)
Introduction to Legal Technology, lecture 1 (2015)Introduction to Legal Technology, lecture 1 (2015)
Introduction to Legal Technology, lecture 1 (2015)
 
What is legal technology?
What is legal technology?What is legal technology?
What is legal technology?
 
Introduction to Legal Technology, lecture 10 (2015)
Introduction to Legal Technology, lecture 10 (2015)Introduction to Legal Technology, lecture 10 (2015)
Introduction to Legal Technology, lecture 10 (2015)
 
Modeling meaning and knowledge: legal knowledge
Modeling meaning and knowledge: legal knowledgeModeling meaning and knowledge: legal knowledge
Modeling meaning and knowledge: legal knowledge
 
Finnish Legal Tech Forum launch presentation
Finnish Legal Tech Forum launch presentationFinnish Legal Tech Forum launch presentation
Finnish Legal Tech Forum launch presentation
 
Introduction to Legal Technology, lecture 5 (2015)
Introduction to Legal Technology, lecture 5 (2015)Introduction to Legal Technology, lecture 5 (2015)
Introduction to Legal Technology, lecture 5 (2015)
 
Introduction to Legal Technology, lecture 6 (2015)
Introduction to Legal Technology, lecture 6 (2015)Introduction to Legal Technology, lecture 6 (2015)
Introduction to Legal Technology, lecture 6 (2015)
 
Introduction to Legal Technology, lecture 9 (2015)
Introduction to Legal Technology, lecture 9 (2015)Introduction to Legal Technology, lecture 9 (2015)
Introduction to Legal Technology, lecture 9 (2015)
 
Commercializing legal AI research: lessons learned
Commercializing legal AI research: lessons learnedCommercializing legal AI research: lessons learned
Commercializing legal AI research: lessons learned
 
Helsinki Legal Tech Meetup introduction
Helsinki Legal Tech Meetup introductionHelsinki Legal Tech Meetup introduction
Helsinki Legal Tech Meetup introduction
 
Helsinki Legal Tech Meetup: TrademarkNow demo/presentation
Helsinki Legal Tech Meetup: TrademarkNow demo/presentationHelsinki Legal Tech Meetup: TrademarkNow demo/presentation
Helsinki Legal Tech Meetup: TrademarkNow demo/presentation
 
Ai and applications in the legal domain studium generale maastricht 20191101
Ai and applications in the legal domain studium generale maastricht 20191101Ai and applications in the legal domain studium generale maastricht 20191101
Ai and applications in the legal domain studium generale maastricht 20191101
 
Legal tech Alliance Workshop 20191029
Legal tech Alliance Workshop 20191029Legal tech Alliance Workshop 20191029
Legal tech Alliance Workshop 20191029
 
eDiscovery
eDiscoveryeDiscovery
eDiscovery
 
A peek into open hardware: Motivations, Licensing, and Ecosystem
A peek into open hardware: Motivations, Licensing, and EcosystemA peek into open hardware: Motivations, Licensing, and Ecosystem
A peek into open hardware: Motivations, Licensing, and Ecosystem
 
HBS seminar 3/26/14: Dark Markets, Bad Patents, No Data
HBS seminar 3/26/14: Dark Markets, Bad Patents, No DataHBS seminar 3/26/14: Dark Markets, Bad Patents, No Data
HBS seminar 3/26/14: Dark Markets, Bad Patents, No Data
 

Viewers also liked

Tietokone korvaa juristin – vai korvaako?
Tietokone korvaa juristin – vai korvaako?Tietokone korvaa juristin – vai korvaako?
Tietokone korvaa juristin – vai korvaako?
Anna Ronkainen
 
Creating products that lawyers love (sic!) – design in legal technology
Creating products that lawyers love (sic!) – design in legal technologyCreating products that lawyers love (sic!) – design in legal technology
Creating products that lawyers love (sic!) – design in legal technology
Anna Ronkainen
 
Introduction to legal design: Product & project management
Introduction to legal design: Product & project managementIntroduction to legal design: Product & project management
Introduction to legal design: Product & project management
Anna Ronkainen
 
Reperes implicit tests - gb - sept 2012
Reperes    implicit tests - gb - sept 2012Reperes    implicit tests - gb - sept 2012
Reperes implicit tests - gb - sept 2012
François Abiven
 
Product management (at Boost Turku Startup Journey 2015)
Product management (at Boost Turku Startup Journey 2015)Product management (at Boost Turku Startup Journey 2015)
Product management (at Boost Turku Startup Journey 2015)
Anna Ronkainen
 
Product management – what makes or breaks a startup
Product management – what makes or breaks a startupProduct management – what makes or breaks a startup
Product management – what makes or breaks a startup
Anna Ronkainen
 
Tavaramerkki. NYT! IPR-palveluihin uusi ulottuvuus
Tavaramerkki. NYT! IPR-palveluihin uusi ulottuvuusTavaramerkki. NYT! IPR-palveluihin uusi ulottuvuus
Tavaramerkki. NYT! IPR-palveluihin uusi ulottuvuus
Anna Ronkainen
 
Tulevaisuus on jo täällä
Tulevaisuus on jo täälläTulevaisuus on jo täällä
Tulevaisuus on jo täällä
Anna Ronkainen
 
BBC Research: The Science of Engagement
BBC Research: The Science of EngagementBBC Research: The Science of Engagement
BBC Research: The Science of Engagement
IAB Europe
 
Open Source IAT - SPSP 2013
Open Source IAT - SPSP 2013Open Source IAT - SPSP 2013
Open Source IAT - SPSP 2013
Winter Mason
 
AI in legal practice – the research perspective
AI in legal practice – the research perspectiveAI in legal practice – the research perspective
AI in legal practice – the research perspective
Anna Ronkainen
 

Viewers also liked (11)

Tietokone korvaa juristin – vai korvaako?
Tietokone korvaa juristin – vai korvaako?Tietokone korvaa juristin – vai korvaako?
Tietokone korvaa juristin – vai korvaako?
 
Creating products that lawyers love (sic!) – design in legal technology
Creating products that lawyers love (sic!) – design in legal technologyCreating products that lawyers love (sic!) – design in legal technology
Creating products that lawyers love (sic!) – design in legal technology
 
Introduction to legal design: Product & project management
Introduction to legal design: Product & project managementIntroduction to legal design: Product & project management
Introduction to legal design: Product & project management
 
Reperes implicit tests - gb - sept 2012
Reperes    implicit tests - gb - sept 2012Reperes    implicit tests - gb - sept 2012
Reperes implicit tests - gb - sept 2012
 
Product management (at Boost Turku Startup Journey 2015)
Product management (at Boost Turku Startup Journey 2015)Product management (at Boost Turku Startup Journey 2015)
Product management (at Boost Turku Startup Journey 2015)
 
Product management – what makes or breaks a startup
Product management – what makes or breaks a startupProduct management – what makes or breaks a startup
Product management – what makes or breaks a startup
 
Tavaramerkki. NYT! IPR-palveluihin uusi ulottuvuus
Tavaramerkki. NYT! IPR-palveluihin uusi ulottuvuusTavaramerkki. NYT! IPR-palveluihin uusi ulottuvuus
Tavaramerkki. NYT! IPR-palveluihin uusi ulottuvuus
 
Tulevaisuus on jo täällä
Tulevaisuus on jo täälläTulevaisuus on jo täällä
Tulevaisuus on jo täällä
 
BBC Research: The Science of Engagement
BBC Research: The Science of EngagementBBC Research: The Science of Engagement
BBC Research: The Science of Engagement
 
Open Source IAT - SPSP 2013
Open Source IAT - SPSP 2013Open Source IAT - SPSP 2013
Open Source IAT - SPSP 2013
 
AI in legal practice – the research perspective
AI in legal practice – the research perspectiveAI in legal practice – the research perspective
AI in legal practice – the research perspective
 

Similar to How to do things with AI & law research

Dispute Resolution Options in the Tech industry
Dispute Resolution Options in the Tech industry Dispute Resolution Options in the Tech industry
Dispute Resolution Options in the Tech industry
Emmanuel Inyada
 
4-28-16 IP for general counsel (publish)
4-28-16 IP for general counsel (publish)4-28-16 IP for general counsel (publish)
4-28-16 IP for general counsel (publish)
Stephen Mason
 
Intellectual Property: Presentation on IP in IT : Global Strategy - BananaIP
Intellectual Property: Presentation on IP in IT : Global Strategy - BananaIPIntellectual Property: Presentation on IP in IT : Global Strategy - BananaIP
Intellectual Property: Presentation on IP in IT : Global Strategy - BananaIP
BananaIP Counsels
 
Trade in ideas
Trade in ideasTrade in ideas
Trade in ideas
Springer
 
Npe antitrust challenges - writing sample
Npe   antitrust challenges - writing sampleNpe   antitrust challenges - writing sample
Npe antitrust challenges - writing sample
BinQiang Liu
 
What Do You Do with a Problem Like AI?
What Do You Do with a Problem Like AI?What Do You Do with a Problem Like AI?
What Do You Do with a Problem Like AI?
Lilian Edwards
 
How IP Litigation Will Be Impacted By New Technologies: AI, Smart Devices, an...
How IP Litigation Will Be Impacted By New Technologies: AI, Smart Devices, an...How IP Litigation Will Be Impacted By New Technologies: AI, Smart Devices, an...
How IP Litigation Will Be Impacted By New Technologies: AI, Smart Devices, an...
Jeremy Elman
 
Intellectual Property Overview.pdf
Intellectual Property Overview.pdfIntellectual Property Overview.pdf
Intellectual Property Overview.pdf
professormadison
 
Lesi 2017 annual conference apr 2017.part 1 (david perkins)
Lesi 2017 annual conference  apr 2017.part 1 (david perkins)Lesi 2017 annual conference  apr 2017.part 1 (david perkins)
Lesi 2017 annual conference apr 2017.part 1 (david perkins)
JAMSInternational
 
Ipdr munich mar 2017 (david perkins)
Ipdr munich mar 2017 (david perkins)Ipdr munich mar 2017 (david perkins)
Ipdr munich mar 2017 (david perkins)
JAMSInternational
 
Law Is Code - Tech for Lawyers Masterclass
Law Is Code - Tech for Lawyers MasterclassLaw Is Code - Tech for Lawyers Masterclass
Law Is Code - Tech for Lawyers Masterclass
Adrien van den Branden
 
Introduction to Legal Technology, lecture 9 (2018)
Introduction to Legal Technology, lecture 9 (2018)Introduction to Legal Technology, lecture 9 (2018)
Introduction to Legal Technology, lecture 9 (2018)
Anna Ronkainen
 
Os Berlin Dispelling Myths
Os Berlin Dispelling MythsOs Berlin Dispelling Myths
Os Berlin Dispelling Myths
oscon2007
 
Intellectual Property
Intellectual PropertyIntellectual Property
Intellectual Property
Upekha Vandebona
 
Are we burying our heads in the sand? Exploring issues around intellectual pr...
Are we burying our heads in the sand? Exploring issues around intellectual pr...Are we burying our heads in the sand? Exploring issues around intellectual pr...
Are we burying our heads in the sand? Exploring issues around intellectual pr...
Jennifer Cham
 
Patents and Trademarks
Patents and TrademarksPatents and Trademarks
Patents and Trademarks
Blake Sorensen
 
IP Protection in the USA
IP Protection in the USAIP Protection in the USA
IP Protection in the USA
Aylin Akturk Sahin
 
On Mapping Values in AI Governance
On Mapping Values in AI GovernanceOn Mapping Values in AI Governance
On Mapping Values in AI Governance
Giovanni Sileno
 
Сергей Уляхин "Аспекты коммерциализации интеллектуальной собственности" (S.Ul...
Сергей Уляхин "Аспекты коммерциализации интеллектуальной собственности" (S.Ul...Сергей Уляхин "Аспекты коммерциализации интеллектуальной собственности" (S.Ul...
Сергей Уляхин "Аспекты коммерциализации интеллектуальной собственности" (S.Ul...
EconMsu
 
Life Sciences News_December_2010
Life Sciences News_December_2010Life Sciences News_December_2010
Life Sciences News_December_2010
LaBron Mathews
 

Similar to How to do things with AI & law research (20)

Dispute Resolution Options in the Tech industry
Dispute Resolution Options in the Tech industry Dispute Resolution Options in the Tech industry
Dispute Resolution Options in the Tech industry
 
4-28-16 IP for general counsel (publish)
4-28-16 IP for general counsel (publish)4-28-16 IP for general counsel (publish)
4-28-16 IP for general counsel (publish)
 
Intellectual Property: Presentation on IP in IT : Global Strategy - BananaIP
Intellectual Property: Presentation on IP in IT : Global Strategy - BananaIPIntellectual Property: Presentation on IP in IT : Global Strategy - BananaIP
Intellectual Property: Presentation on IP in IT : Global Strategy - BananaIP
 
Trade in ideas
Trade in ideasTrade in ideas
Trade in ideas
 
Npe antitrust challenges - writing sample
Npe   antitrust challenges - writing sampleNpe   antitrust challenges - writing sample
Npe antitrust challenges - writing sample
 
What Do You Do with a Problem Like AI?
What Do You Do with a Problem Like AI?What Do You Do with a Problem Like AI?
What Do You Do with a Problem Like AI?
 
How IP Litigation Will Be Impacted By New Technologies: AI, Smart Devices, an...
How IP Litigation Will Be Impacted By New Technologies: AI, Smart Devices, an...How IP Litigation Will Be Impacted By New Technologies: AI, Smart Devices, an...
How IP Litigation Will Be Impacted By New Technologies: AI, Smart Devices, an...
 
Intellectual Property Overview.pdf
Intellectual Property Overview.pdfIntellectual Property Overview.pdf
Intellectual Property Overview.pdf
 
Lesi 2017 annual conference apr 2017.part 1 (david perkins)
Lesi 2017 annual conference  apr 2017.part 1 (david perkins)Lesi 2017 annual conference  apr 2017.part 1 (david perkins)
Lesi 2017 annual conference apr 2017.part 1 (david perkins)
 
Ipdr munich mar 2017 (david perkins)
Ipdr munich mar 2017 (david perkins)Ipdr munich mar 2017 (david perkins)
Ipdr munich mar 2017 (david perkins)
 
Law Is Code - Tech for Lawyers Masterclass
Law Is Code - Tech for Lawyers MasterclassLaw Is Code - Tech for Lawyers Masterclass
Law Is Code - Tech for Lawyers Masterclass
 
Introduction to Legal Technology, lecture 9 (2018)
Introduction to Legal Technology, lecture 9 (2018)Introduction to Legal Technology, lecture 9 (2018)
Introduction to Legal Technology, lecture 9 (2018)
 
Os Berlin Dispelling Myths
Os Berlin Dispelling MythsOs Berlin Dispelling Myths
Os Berlin Dispelling Myths
 
Intellectual Property
Intellectual PropertyIntellectual Property
Intellectual Property
 
Are we burying our heads in the sand? Exploring issues around intellectual pr...
Are we burying our heads in the sand? Exploring issues around intellectual pr...Are we burying our heads in the sand? Exploring issues around intellectual pr...
Are we burying our heads in the sand? Exploring issues around intellectual pr...
 
Patents and Trademarks
Patents and TrademarksPatents and Trademarks
Patents and Trademarks
 
IP Protection in the USA
IP Protection in the USAIP Protection in the USA
IP Protection in the USA
 
On Mapping Values in AI Governance
On Mapping Values in AI GovernanceOn Mapping Values in AI Governance
On Mapping Values in AI Governance
 
Сергей Уляхин "Аспекты коммерциализации интеллектуальной собственности" (S.Ul...
Сергей Уляхин "Аспекты коммерциализации интеллектуальной собственности" (S.Ul...Сергей Уляхин "Аспекты коммерциализации интеллектуальной собственности" (S.Ul...
Сергей Уляхин "Аспекты коммерциализации интеллектуальной собственности" (S.Ul...
 
Life Sciences News_December_2010
Life Sciences News_December_2010Life Sciences News_December_2010
Life Sciences News_December_2010
 

Recently uploaded

Indonesian Manpower Regulation on Severance Pay for Retiring Private Sector E...
Indonesian Manpower Regulation on Severance Pay for Retiring Private Sector E...Indonesian Manpower Regulation on Severance Pay for Retiring Private Sector E...
Indonesian Manpower Regulation on Severance Pay for Retiring Private Sector E...
AHRP Law Firm
 
一比一原版(monash毕业证书)莫纳什大学毕业证如何办理
一比一原版(monash毕业证书)莫纳什大学毕业证如何办理一比一原版(monash毕业证书)莫纳什大学毕业证如何办理
一比一原版(monash毕业证书)莫纳什大学毕业证如何办理
bzofm
 
一比一原版(uwgb毕业证书)美国威斯康星大学绿湾分校毕业证如何办理
一比一原版(uwgb毕业证书)美国威斯康星大学绿湾分校毕业证如何办理一比一原版(uwgb毕业证书)美国威斯康星大学绿湾分校毕业证如何办理
一比一原版(uwgb毕业证书)美国威斯康星大学绿湾分校毕业证如何办理
pdeehy
 
一比一原版(ua毕业证书)加拿大阿尔伯塔大学毕业证如何办理
一比一原版(ua毕业证书)加拿大阿尔伯塔大学毕业证如何办理一比一原版(ua毕业证书)加拿大阿尔伯塔大学毕业证如何办理
一比一原版(ua毕业证书)加拿大阿尔伯塔大学毕业证如何办理
ubype
 
一比一原版(uottawa毕业证书)加拿大渥太华大学毕业证如何办理
一比一原版(uottawa毕业证书)加拿大渥太华大学毕业证如何办理一比一原版(uottawa毕业证书)加拿大渥太华大学毕业证如何办理
一比一原版(uottawa毕业证书)加拿大渥太华大学毕业证如何办理
uhsox
 
一比一原版新加坡国立大学毕业证(本硕)nus学位证书如何办理
一比一原版新加坡国立大学毕业证(本硕)nus学位证书如何办理一比一原版新加坡国立大学毕业证(本硕)nus学位证书如何办理
一比一原版新加坡国立大学毕业证(本硕)nus学位证书如何办理
ucoux1
 
一比一原版英国伦敦大学亚非学院毕业证(soas毕业证书)如何办理
一比一原版英国伦敦大学亚非学院毕业证(soas毕业证书)如何办理一比一原版英国伦敦大学亚非学院毕业证(soas毕业证书)如何办理
一比一原版英国伦敦大学亚非学院毕业证(soas毕业证书)如何办理
duxss
 
一比一原版加拿大多伦多大学毕业证(uoft毕业证书)如何办理
一比一原版加拿大多伦多大学毕业证(uoft毕业证书)如何办理一比一原版加拿大多伦多大学毕业证(uoft毕业证书)如何办理
一比一原版加拿大多伦多大学毕业证(uoft毕业证书)如何办理
onduyv
 
Comparative analysis of ipc and bharitye Naya sahinta
Comparative analysis of ipc and bharitye Naya sahintaComparative analysis of ipc and bharitye Naya sahinta
Comparative analysis of ipc and bharitye Naya sahinta
adi2292
 
一比一原版(glasgow毕业证书)格拉斯哥大学毕业证如何办理
一比一原版(glasgow毕业证书)格拉斯哥大学毕业证如何办理一比一原版(glasgow毕业证书)格拉斯哥大学毕业证如何办理
一比一原版(glasgow毕业证书)格拉斯哥大学毕业证如何办理
ooqzo
 
Asian legal busiess india you are invited
Asian legal busiess india you are invitedAsian legal busiess india you are invited
Asian legal busiess india you are invited
digitalrashi12
 
一比一原版(trent毕业证书)加拿大特伦特大学毕业证如何办理
一比一原版(trent毕业证书)加拿大特伦特大学毕业证如何办理一比一原版(trent毕业证书)加拿大特伦特大学毕业证如何办理
一比一原版(trent毕业证书)加拿大特伦特大学毕业证如何办理
mecyyn
 
It's the Law: Recent Court and Administrative Decisions of Interest
It's the Law: Recent Court and Administrative Decisions of InterestIt's the Law: Recent Court and Administrative Decisions of Interest
It's the Law: Recent Court and Administrative Decisions of Interest
Parsons Behle & Latimer
 
一比一原版英国伦敦商学院毕业证(lbs毕业证书)如何办理
一比一原版英国伦敦商学院毕业证(lbs毕业证书)如何办理一比一原版英国伦敦商学院毕业证(lbs毕业证书)如何办理
一比一原版英国伦敦商学院毕业证(lbs毕业证书)如何办理
gedsuu
 
一比一原版(uwlc毕业证书)美国威斯康星大学拉克罗斯分校毕业证如何办理
一比一原版(uwlc毕业证书)美国威斯康星大学拉克罗斯分校毕业证如何办理一比一原版(uwlc毕业证书)美国威斯康星大学拉克罗斯分校毕业证如何办理
一比一原版(uwlc毕业证书)美国威斯康星大学拉克罗斯分校毕业证如何办理
qevye
 
THE CONCEPT OF RIGHT TO DEFAULT BAIL.pptx
THE CONCEPT OF RIGHT TO DEFAULT BAIL.pptxTHE CONCEPT OF RIGHT TO DEFAULT BAIL.pptx
THE CONCEPT OF RIGHT TO DEFAULT BAIL.pptx
Namrata Chakraborty
 
原版定做(sheffield学位证书)英国谢菲尔德大学毕业证文凭证书原版一模一样
原版定做(sheffield学位证书)英国谢菲尔德大学毕业证文凭证书原版一模一样原版定做(sheffield学位证书)英国谢菲尔德大学毕业证文凭证书原版一模一样
原版定做(sheffield学位证书)英国谢菲尔德大学毕业证文凭证书原版一模一样
abondo3
 
一比一原版伯恩茅斯大学毕业证(bu毕业证)如何办理
一比一原版伯恩茅斯大学毕业证(bu毕业证)如何办理一比一原版伯恩茅斯大学毕业证(bu毕业证)如何办理
一比一原版伯恩茅斯大学毕业证(bu毕业证)如何办理
ymefneb
 
一比一原版加拿大达尔豪斯大学毕业证(dalhousie毕业证书)如何办理
一比一原版加拿大达尔豪斯大学毕业证(dalhousie毕业证书)如何办理一比一原版加拿大达尔豪斯大学毕业证(dalhousie毕业证书)如何办理
一比一原版加拿大达尔豪斯大学毕业证(dalhousie毕业证书)如何办理
cadyzeo
 
Capital Punishment by Saif Javed (LLM)ppt.pptx
Capital Punishment by Saif Javed (LLM)ppt.pptxCapital Punishment by Saif Javed (LLM)ppt.pptx
Capital Punishment by Saif Javed (LLM)ppt.pptx
OmGod1
 

Recently uploaded (20)

Indonesian Manpower Regulation on Severance Pay for Retiring Private Sector E...
Indonesian Manpower Regulation on Severance Pay for Retiring Private Sector E...Indonesian Manpower Regulation on Severance Pay for Retiring Private Sector E...
Indonesian Manpower Regulation on Severance Pay for Retiring Private Sector E...
 
一比一原版(monash毕业证书)莫纳什大学毕业证如何办理
一比一原版(monash毕业证书)莫纳什大学毕业证如何办理一比一原版(monash毕业证书)莫纳什大学毕业证如何办理
一比一原版(monash毕业证书)莫纳什大学毕业证如何办理
 
一比一原版(uwgb毕业证书)美国威斯康星大学绿湾分校毕业证如何办理
一比一原版(uwgb毕业证书)美国威斯康星大学绿湾分校毕业证如何办理一比一原版(uwgb毕业证书)美国威斯康星大学绿湾分校毕业证如何办理
一比一原版(uwgb毕业证书)美国威斯康星大学绿湾分校毕业证如何办理
 
一比一原版(ua毕业证书)加拿大阿尔伯塔大学毕业证如何办理
一比一原版(ua毕业证书)加拿大阿尔伯塔大学毕业证如何办理一比一原版(ua毕业证书)加拿大阿尔伯塔大学毕业证如何办理
一比一原版(ua毕业证书)加拿大阿尔伯塔大学毕业证如何办理
 
一比一原版(uottawa毕业证书)加拿大渥太华大学毕业证如何办理
一比一原版(uottawa毕业证书)加拿大渥太华大学毕业证如何办理一比一原版(uottawa毕业证书)加拿大渥太华大学毕业证如何办理
一比一原版(uottawa毕业证书)加拿大渥太华大学毕业证如何办理
 
一比一原版新加坡国立大学毕业证(本硕)nus学位证书如何办理
一比一原版新加坡国立大学毕业证(本硕)nus学位证书如何办理一比一原版新加坡国立大学毕业证(本硕)nus学位证书如何办理
一比一原版新加坡国立大学毕业证(本硕)nus学位证书如何办理
 
一比一原版英国伦敦大学亚非学院毕业证(soas毕业证书)如何办理
一比一原版英国伦敦大学亚非学院毕业证(soas毕业证书)如何办理一比一原版英国伦敦大学亚非学院毕业证(soas毕业证书)如何办理
一比一原版英国伦敦大学亚非学院毕业证(soas毕业证书)如何办理
 
一比一原版加拿大多伦多大学毕业证(uoft毕业证书)如何办理
一比一原版加拿大多伦多大学毕业证(uoft毕业证书)如何办理一比一原版加拿大多伦多大学毕业证(uoft毕业证书)如何办理
一比一原版加拿大多伦多大学毕业证(uoft毕业证书)如何办理
 
Comparative analysis of ipc and bharitye Naya sahinta
Comparative analysis of ipc and bharitye Naya sahintaComparative analysis of ipc and bharitye Naya sahinta
Comparative analysis of ipc and bharitye Naya sahinta
 
一比一原版(glasgow毕业证书)格拉斯哥大学毕业证如何办理
一比一原版(glasgow毕业证书)格拉斯哥大学毕业证如何办理一比一原版(glasgow毕业证书)格拉斯哥大学毕业证如何办理
一比一原版(glasgow毕业证书)格拉斯哥大学毕业证如何办理
 
Asian legal busiess india you are invited
Asian legal busiess india you are invitedAsian legal busiess india you are invited
Asian legal busiess india you are invited
 
一比一原版(trent毕业证书)加拿大特伦特大学毕业证如何办理
一比一原版(trent毕业证书)加拿大特伦特大学毕业证如何办理一比一原版(trent毕业证书)加拿大特伦特大学毕业证如何办理
一比一原版(trent毕业证书)加拿大特伦特大学毕业证如何办理
 
It's the Law: Recent Court and Administrative Decisions of Interest
It's the Law: Recent Court and Administrative Decisions of InterestIt's the Law: Recent Court and Administrative Decisions of Interest
It's the Law: Recent Court and Administrative Decisions of Interest
 
一比一原版英国伦敦商学院毕业证(lbs毕业证书)如何办理
一比一原版英国伦敦商学院毕业证(lbs毕业证书)如何办理一比一原版英国伦敦商学院毕业证(lbs毕业证书)如何办理
一比一原版英国伦敦商学院毕业证(lbs毕业证书)如何办理
 
一比一原版(uwlc毕业证书)美国威斯康星大学拉克罗斯分校毕业证如何办理
一比一原版(uwlc毕业证书)美国威斯康星大学拉克罗斯分校毕业证如何办理一比一原版(uwlc毕业证书)美国威斯康星大学拉克罗斯分校毕业证如何办理
一比一原版(uwlc毕业证书)美国威斯康星大学拉克罗斯分校毕业证如何办理
 
THE CONCEPT OF RIGHT TO DEFAULT BAIL.pptx
THE CONCEPT OF RIGHT TO DEFAULT BAIL.pptxTHE CONCEPT OF RIGHT TO DEFAULT BAIL.pptx
THE CONCEPT OF RIGHT TO DEFAULT BAIL.pptx
 
原版定做(sheffield学位证书)英国谢菲尔德大学毕业证文凭证书原版一模一样
原版定做(sheffield学位证书)英国谢菲尔德大学毕业证文凭证书原版一模一样原版定做(sheffield学位证书)英国谢菲尔德大学毕业证文凭证书原版一模一样
原版定做(sheffield学位证书)英国谢菲尔德大学毕业证文凭证书原版一模一样
 
一比一原版伯恩茅斯大学毕业证(bu毕业证)如何办理
一比一原版伯恩茅斯大学毕业证(bu毕业证)如何办理一比一原版伯恩茅斯大学毕业证(bu毕业证)如何办理
一比一原版伯恩茅斯大学毕业证(bu毕业证)如何办理
 
一比一原版加拿大达尔豪斯大学毕业证(dalhousie毕业证书)如何办理
一比一原版加拿大达尔豪斯大学毕业证(dalhousie毕业证书)如何办理一比一原版加拿大达尔豪斯大学毕业证(dalhousie毕业证书)如何办理
一比一原版加拿大达尔豪斯大学毕业证(dalhousie毕业证书)如何办理
 
Capital Punishment by Saif Javed (LLM)ppt.pptx
Capital Punishment by Saif Javed (LLM)ppt.pptxCapital Punishment by Saif Javed (LLM)ppt.pptx
Capital Punishment by Saif Javed (LLM)ppt.pptx
 

How to do things with AI & law research

  • 1. How to do things with AI & law research Dutch Legal Tech Meetup, 2015-11-02 Anna Ronkainen Chief Scientist, TrademarkNow Inc. @ronkaine
  • 2. A tale of two origin stories
  • 3. ‘Preliminary try-outs of decision machines built according to various formal specifications can be made in relation to selected administrative or judicial tribunals. The Supreme Court might be chosen for the purpose.’ (Harold Lasswell 1955)
  • 4. ‘Can we “feed” into the computer that the judge’s ulcer is getting worse, that he had fought earlier in the morning with his wife, that the coffee was cold, that the defence counsel is an apparent moron, that the temporarily assigned associate judge is unfamiliar with the law and besides smokes obnoxious cigars, that the tailor’s bill was outrageous etc. etc.?’ (Kaarle Makkonen 1968, translation ar)
  • 5. ”As we know, there are known knowns. There are things we know we know. We also know there are known unknowns, that is to say, we know there are some things we do not know. But there are also unknown unknowns, the ones we don’t know we don’t know.” – Donald Rumsfeld (2002)
  • 9. Dual-process cognition System 1 •  evolutionarily old •  unconscious, preconscious •  shared with animals •  implicit knowledge •  automatic •  fast •  parallel •  high capacity •  intuitive •  contextualized •  pragmatic •  associative •  independent of general intelligence System 2 •  evolutionarily recent •  conscious •  distinctively human •  explicit knowledge •  controlled •  slow •  sequential •  low capacity •  reflective •  abstract •  logical •  rule-based •  linked to general intelligence (Frankish & Evans 2009)
  • 10. Systems 1 and 2 in legal reasoning: interaction System 1: making the decision System 2: validation and justification (Ronkainen 2011)
  • 11. What’s that got to do with legal AI? -  MOSONG, my 1st (and so far only) system prototype -  built for studying the use of fuzzy logic in modelling various issues in legal theory -  specifically, the use of Type-2 fuzzy logic for modelling vagueness and uncertainty -  trademarks initially just a random example domain -  but the knowledge acquired through this research also proved useful for TrademarkNow...
  • 12. Open texture ‘Whichever device, precedent or legislation, is chosen for the communication of standards of behaviour, these, however smoothly they work over the great mass of ordinary cases, will, at some point where their application is in question, prove indeterminate; they will have what has been termed an open texture.’ - (Hart 1961)
  • 13. Standard example of open texture : No vehicles in a park ‘When we are bold enough to frame some general rule of conduct (e.g. a rule that no vehicle may be taken into the park), the language used in this context fixes necessary conditions which anything must satisfy if it is to be within its scope, and certain clear examples of what is certainly within its scope may be present to our minds.’ (Hart 1961) ... but that’s really a stupid example because vehicles are already categorized in excruciating detail so being more precise costs nothing
  • 14. Inescapable open texture: No boozing in a park (but “civilized” drinking is okay) Section 4 Intake of intoxicating substances The intake of intoxicating substances is prohibited in public places in built-up areas [...]. The provisions of paragraph 1 do not concern [...] the intake of alcoholic beverages in a park or in a comparable public place in a manner such that the intake or the presence associated with it does not obstruct or unreasonably encumber other persons’ right to use the place for its intended purpose. (Finland: Public Order Act (612/2003))
  • 15. Inescapable open texture: Trademark similarity (Mosong) Article 8 Relative grounds for refusal 1. Upon opposition by the proprietor of an earlier trade mark, the trade mark applied for shall not be registered: (a) if it is identical with the earlier trade mark and the goods or services for which registration is applied for are identical with the goods or services for which the earlier trade mark is protected; (b) if because of its identity with or similarity to the earlier trade mark and the identity or similarity of the goods or services covered by the trade marks there exists a likelihood of confusion on the part of the public in the territory in which the earlier trade mark is protected; the likelihood of confusion includes the likelihood of association with the earlier trade mark. [...] (CTM Regulation (40/94/EC))
  • 16. Mosong: the domain Tentative rule Article 8 Relative grounds for refusal 1. Upon opposition by the proprietor of an earlier trade mark, the trade mark applied for shall not be registered: (a) if it is identical with the earlier trade mark and the goods or services for which registration is applied for are identical with the goods or services for which the earlier trade mark is protected; (b) if because of its identity with or similarity to the earlier trade mark and the identity or similarity of the goods or services covered by the trade marks there exists a likelihood of confusion on the part of the public in the territory in which the earlier trade mark is protected; the likelihood of confusion includes the likelihood of association with the earlier trade mark. REFUSAL = MARKS-SIMILAR and GOODS-SIMILAR
  • 18. “Training set” 119 OHIM cases from 1997–2000, of which 107 from the Opposition Division (1st instance) and 12 from the Boards of Appeal (2nd instance)
  • 19. Results for the training set 0 0.2 0.4 0.6 0.8 1
  • 20. Validation set 30 most recent (2002) relevant cases from OHIM: 20 from the Opposition Division and 10 from the Boards of Appeal Result*: all cases predicted correctly * when coded into the system by a domain expert
  • 21. Results for the validation set 0 0.2 0.4 0.6 0.8 1
  • 22. Non-expert validation •  done by non-law students taking a course on •  intellectual property law (n=75) •  original validation set in two parts (15+15 cases) •  at the beginning and the end of the course •  completed non-interactively through a web form •  correct answer: 54.6±6.5% •  incorrect answer: 25.9±7.5% •  no answer: 19.5±5.2% (± = σ)
  • 23. Non-expert validation % ±stderr before after total group 1 (n=15) 41.3±1.7 65.8±2.8 53.5±1.7 group 2 (n=12) 46.1±2.0 65.0±3.0 55.6±1.9 group 3 (n=48) 43.3±1.3 65.9±1.3 54.7±0.9 total (n=75) 43.4±1.0 65.8±1.1 54.6±0.8
  • 24. Initial conclusions from this work -  it (sort of) works; using fuzzy logic makes sense in this context -  poses more questions than it answers... -  ...and that’s how I ended up trying to reverse-engineer human lawyers rather than just trying to build systems based on existing legal theory literature
  • 25. Implications for legal AI -  using rule-based methods has its advantages -  human-readable -  comparatively quick to develop -  modifiable (esp. relevant wrt legislative changes) -  but they can’t do the work alone -  can’t make sense about situations which they weren’t specifically built to handle -  real-world complexity needs (sometimes) statistical/machine-learning approaches
  • 26. That second origin story...
  • 27. It started with a frustrated trademark attorney... -  Mikael Kolehmainen – now TMnow CEO – worked as a trademark attorney at one of the biggest boutique TM firms in Finland -  he was deeply frustrated with the existing way of doing trademark searches and wanted to do something about it -  first he found Matti Kokkola (CTO) through a common acquintance -  then me (Chief Scientist) via the university, totally by accident -  and finally Heikki Vesalainen (Chief Architect) who had co- founded a company with Matti as sophomores -  development work started spring 2012, seed funding from Lifeline Ventures in August 2012 -  first release (then as Onomatics Quick Search) Oct 2012
  • 28. So, what came out of it?
  • 29. About TrademarkNow -  trademark legal technology provider founded in 2012, based in Helsinki, NYC and Kilkenny, now ~30 employees -  total funding so far ~3MEUR (plus gov’t grants and loans (Tekes)) -  products based on a unique model of likelihood of confusion for trademarks -  NameCheck: intelligent TM search -  NameWatch: intelligent TM watch
  • 30. Trademark search: Ye olde way -  TM lawyer formulates search strategy (wildcards & classes to be used etc.) -  paralegal carries out the search and hands the results to the lawyer -  lawyer browses through the results and marks the ones for which more info needed -  paralegal retrieves additional info -  lawyer reviews said info, evaluates risk, reports -  whole process typically takes several days, stakeholders often expect results immediately
  • 34. Core component: AI model of likelihood of confusion (TM similarity) -  similarity of trademarks -  phonetical -  graphic -  semantic -  currently only word marks -  similarity of goods and services -  others do this only using the 45 classes of the Nice Classification
  • 35. Mix-and-match approach to AI techniques -  traditionally AI has been divided into to factions: rule-based and statistical -  all our competitors are also either-or -  both have their advantages and disadvantages -  unlike the mainstream, we’re flexible and use both as we see fit, to maximize their benefits
  • 39. Research commercialization is difficult in general – not only for AI & law -  innovation and commercialization are tossed around as vital research policy goals a lot these days pretty much wherever you go -  said tossers* tend to treat it as a black box, basically thinking that telling academics to be innovative is all it takes -  there are two parts in the equation, and only one of them can be said to be the academics’ responsibility * sorry, couldn’t resist
  • 40. 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
  • 41. The first part of the equation: 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...)
  • 42. 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)
  • 43. 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
  • 44. Een kans uitzonderlijk voor Nederland -  veel geld geïnvesteerd in rechtsinformatisch wetenschappelijk onderzoek in de jaren 1980 -  de gerichte investering nu weg maar de sporen zijn nog duidelijk te zien in het AI & law-milieu (bijv. JURIX) -  meerdere succesvolle projecten in samenwerking met de overheid -  maar (bijna?) geen startups met oorsprong in het onderzoeksmilieu -  dus potentieel een enorme bron van expertise voor nieuwe juridische startups te gebruiken