WISPER receives funding from the European Commission’s Information Societies Technology (IST) Programme IST WISPER Dr Gary Randall British Maritime Technology Dr Luc Grivel TEMIS, France Worldwide Intelligent Semantic Patent Extraction Retrieval Start date : May 2002

WISPER receives funding from the European Commission’s Information Societies Technology (IST) Programme IST WISPER Objectives An intelligent web-based search system for patent databases –Will radically alter the mechanisms of access to and ease of use and ACCURACY of patent searches –To re-engineer patent knowledge space allowing multilingual, textual and image based searches –Build extra semantic information into patent archives enabling a graphical visualisation of results

WISPER receives funding from the European Commission’s Information Societies Technology (IST) Programme IST Main Innovations Textual analysis of a very large but limited domain WISPER will enable the first ever search of figures and drawings in patents User profiling allows the search experience to be tailored to each user (areas of interest, browsing pathways) etc Multilingual – 4 languages, SME can search foreign patents in their native language Results presented as linked conceptual cluster maps – user is ‘informed’ by WISPER

WISPER receives funding from the European Commission’s Information Societies Technology (IST) Programme IST Results, Target Domains Post-project – an e-commerce site owned by the partners in a limited company offering WISPER to SMEs, Patent Agents, Patent Offices –Cheap, efficient, flexible use by SMEs –Encourages patenting (currently low), competitiveness –Patents are $3 each to view at derwent.com –Patent agents (lawyers) are very expensive –SME no longer needs a lawyer to do Prior Art search –WISPER will make search by Patent Agents much easier and quicker –WISPER technology will be usable by multiple Patent Offices

WISPER receives funding from the European Commission’s Information Societies Technology (IST) Programme IST Patent Text Mining  Analyse SME needs and examine specificities of « patent language » (which can be implemented by extraction rules)  Define the WISPER metadata format, reformatting & field normalization & additional fields in XML/RDF  Define use case scenarios based on text mining functions : clustering/classification based navigation (hyper-linking), dynamic filtering

WISPER receives funding from the European Commission’s Information Societies Technology (IST) Programme IST Patent Image Processing AIM? An automated interactive search system, no text-tagging (laborious, subjective) (1) Computer Science vs (2) Cognitive Science Fingerprinting (global) Largely Object based (indexing) figure/ground Derive similarity binding of low level features YES: find me something like this YES: Find me an object X NO: Find me an object X NO: find me X in various contexts Reconciliation? WISPER will use both approaches (exactly how is confidential!)

WISPER receives funding from the European Commission’s Information Societies Technology (IST) Programme IST FP6 views – FP6 research must address what do people *really* do to derive understanding if we expect documents to ‘behave’ like us? –Semantics is subjective & fuzzy and not understood in AI –‘Theories’ of semantic understanding must pre- date tools, i.e. how does context determine meaning

WISPER receives funding from the European Commission’s Information Societies Technology (IST) Programme IST Consortium details –British Maritime Technology (UK) Co-ordinator + Image –Innovation Dev. Ltd (UK) End User, Patent Lawyers –TXT e-solutions (ITALY) IT Infrastructure –TEMIS (FRANCE) Text Mining, Visualisation –HAPTICA Robotics (IRELAND) End User –Synthema S. r. L. (ITALY) Translation –Consorzio Milano Ricerche (ITALY) User Profiling