Gerrit Schutte OHIM 9th of December, 2011 Trademark terminology control

Agenda Trademark terminology(4 slides) Work areas(4 slides) Examples(5 slides)

Terminology versus Trademark Terminology Trademarks id class42 termComputer software design … domain name formation; Computer software design; services relating to the design of software artefacts…

Volumes Terminology Trademark ~30 logical term bases under consideration ~15 multi-lingual term bases 22 languages supported in filing the largest term base being OHIM’s harmonized term base to contain ~50,000 multi-lingual entities 100,000 marks p.a. electronically filed to contain ~15 input expressions per mark translated in 22 languages for publication 17,000 oppositions p.a.

Prominent lifecycle events Examination Filing Translation Opposition General publicExaminerTranslatorLegal staff

Key figures and efforts Examination of marks —~ 58% of all expressions automatically matched to reference terminology —~ 50 persons to examine remainder Translation of marks —~68% of all input expressions automatically matched to translation memory —~0.5 * 10 6 of expressions translated into 21 targets Opposition to marks —~17,000 marks opposed to by earlier right holders —~ 50 persons to take opposition decisions pair wise assessing expressions Filing of marks —~ 1.5 * 10 6 input expression electronically filed per annum —~ persons to explore, choose and “invent” terminology

Work areas and dependencies Resource Tooling Linguistics

Resource Consolidation Establish and enforce syntactic rules Eliminate duplicates Expansion Highly frequent terms Market reflection Alignment Compare across organisations Collaboratively build common asset Semantic enrichment Conceptualize Impose and maintain relations building ontology Blacklist Manage terms that are not acceptable

Tooling Parsing Multi field search Auto completion & spell checking Drilling Group and count Allow to post filter Alternatives Re-use previous search input Re-use previous marks as to propose alternative reference terms Ranking Consider multiple weighted fields in result ranking Performance Prioritize quick online response in taking feature decisions

Linguistics Canonization Agree on rules of equivalency To comprise capitalization, punctuation, diacritics Segmentation Frequent use of non authorized segmentators Considerate risk of false positives Morphology Considerate impact Needed in 22 languages Compounding Significant impact in “germanic” languages Requires lexical resources Language resources Manage synonyms, partial synonyms Manage functional phrases

Example: Examination assistance

Example: Alignment

Example: Conceptualization

Example: Broad/Narrow relation

Example: Similarity relation 1. Nature 2. Purpose 3. Method of use 4. Complementary 5. In competition 6. Distribution channels 7. End user 8. Producer

Conclusions The more functional features are made available at front office the more efficient and consistent the proceeding at back office becomes Functional features at large depend on resource maintenance Full maintenance governance requires native speakers (terminologists) for all languages to be accommodated Terminology alignment across organisations requires alignment on matching algorithms alike Linguistic measures taken are domain as well as context of use dependent Language resources, though, may be shared