Presentation about TrademarkNow and its research background given at the CodeX Center for Legal Informatics at Stanford University 2015-06-04.

1. TrademarkNow
(and its research background)
CodeX at Stanford University 2015-06-04
Anna Ronkainen @ronkaine
Chief Scientist and Co-Founder, TrademarkNow
anna.ronkainen@trademarknow.com

2. The real innovator’s dilemma
1. do research
2. ...
3. profit!

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)

6. (Un)known (un)knowns
known
unknowns
known
knowns
unknown
unknowns
??

7. (Un)known (un)knowns
known
unknowns
known
knowns
unknown
unknowns
unknown
knowns

8. (Un)known (un)knowns
conscious
ignorance
conscious
knowledge
unconscious
ignorance
unconscious
knowledge

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 a bad 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 unreasonably encumber
other persons’ right to use the place for its intended
purpose.
(Finland: Public Order Act (612/2003))

15. Mosong: the domain
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

17. ‘Training’ set: 119 cases

18. “Training set”
119 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:
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. So, about that “...” ...

27. About TrademarkNow
- founded in 2012, based in Helsinki, NYC
and Kilkenny, now ~30 employees
- products based on an AI model of likelihood
of confusion for trademarks, based on my
own basic research in computational legal
theory (since 2002)
- NameCheck: intelligent TM search
- NameWatch: intelligent TM watch

28. A month ago, this happened...

29. How trademark searching is
conventionally done
- wildcards!
- Nice classification
- trademark registries
- lots of back-and-forth between a lawyer and a
paralegal (typically taking 2–7 days altogether):
- Lawyer: create search strategy
- Paralegal: carry out search
- L: evaluate results, request more info on most
significant ones
- P: produce more info (repeat as needed)
- L: give final risk assessment

30. Our version:
From the query DAGNIAUX, yogurts, EU

39. Questions?
Thank you!