TrademarkNow (and its research background)

TrademarkNow (and its research background) CodeX at Stanford University 2015-06-04 Anna Ronkainen @ronkaine Chief Scientist and Co-Founder, TrademarkNow [email protected]

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

‘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

??


known unknowns

known knowns

unknown unknowns

unknown 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 a bad 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 unreasonably encumber other persons’ right to use the place for its intended purpose.

(Finland: Public Order Act (612/2003))

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))

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 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

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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

Results for the validation set

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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

So, about that “...” ...

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

A month ago, this happened...

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

Our version: From the query DAGNIAUX, yogurts, EU

Questions? Thank you!