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PATENT SEARCHES Istituto Nazionale Fisica Nucleare 17.12.2012 Rossella Osella
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Roadmap INTRODUCTION TO PATENT SEARCHES ORBIT Patent searches portal and IP Business Intelligence SEARCH EXAMPLES 2
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The role of patents Protection and economic exploitation of the inventions Sources of information: technical, scientific, business publicly available 3
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Importance of patent information Volume Content Accessibility First account 4
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Use of patent information Survey state of the art competitors’ activities Identify solutions to technical problems licensing opportunities Avoid duplication of R&D efforts infringements 5
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Patent databases Worldwide bibliographic Search fields: bibliographic data, title and abstract (as published by the inventor or enriched by the producer) Full text Search fields: bibliographic data, title, abstract (as published by the inventor), description, claims 6
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Operators Bibliographic data Keywords Search fields Classification 7
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Patent search types State of the art Patentability Freedom of use Bibliographic searches Opposition and validity 8 Landscape and mapping
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Fampat: patent family database One database FamPat Legal Status information 45 countries Full Text Data 21 countries Bibliographic patent data 95 countries Patent copies 40 countries Citations 20 countries US reassignments
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Language technology Machine translations Multilingual wizard Key-content Concepts
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IP Business Intelligence More Charts Filing trend analysis: multi-dimensional views by publication and priority date, publication and priority country Major players identification: Inventors, Assignees, Agents Technology analysis: IPC, ECLA, USCLASS codes Concepts analysis Legal Status analysis Citation analysis Full interactivity with search module Mapping and Clustering based on similarity 12
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Search examples Novelty search Are there relevant patents in the field of detection of high energy particles by hybrid diamond-silicon devices? Landscaping Overview on radiation dosimeters: technologies and applications
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Search strategy
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Search history
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NOVELTY SEARCH
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Search hit from step 1
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LANDSCAPING
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Top 30 Assignees
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Top 30 Priority countries
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Distribution by publication date
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G01T-001/00 Measuring X-radiation, gamma radiation, corpuscular radiation or cosmic radiation Top 75 Concepts A61N-005/10 Radiation therapy X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
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Mapping
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Grazie www.questel.com rosella@questel.com
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Key content Extracted from US, EP, WO, full-text in English Identify key sentences describing patent object, advantages and drawbacks, independent claims Uses morpho-syntactic analysis to spot important sentences Good compromise between conciseness of bibliographic abstracts and full text
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FamPat Concepts – Field available in FamPat – Extracted from US, EP, WO, full-text in English – The Concept Field contains keywords and multiword phrases that can be used in searching, browsing and in analysing
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Concept extraction Main idea: extract concepts rather than words as a semantic model of patent documents, weight concepts according to sentence type To deal with large volume we need to do this upfront when loading the data
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Semantic concept tagging « noun phrases » identification – Part-of-speech + stemming – Verb Suppression, some adjectives – Suppress typical“patent” terminology « preferred embodiment », « skilled artisan » …. – Syntactic normalization : « surface of screens » « screen surface » Relevance score computation, based on: – Field, key sentence morpho-syntactic detection, and number of occurrences
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Concept extraction example The invention relates generally to molecular level cleaning of parts by vapor degreasing. More particularly, the invention relates to a solvent mixture comprising n-propyl bromide, a [mixture of low boiling solvents] and, … The solvent mixture of the invention is non- flammable, non-corrosive, non-hazardous, and has a low ozone depletion potential.
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Normalization examples the heat conductivity the conduction of heat heat conductivity heat conduction HEAT CONDUCTION the user of the cellular telephone cellular phone users a user of a mobile telephone any mobile phone users their user mobile telephones MOBILE PHONE USER
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Concepts Each key concept is “scored” Score calculation based on: – Occurrences of the concept in the doc – Fields where the concepts appear – Frequency of the multiword concept in the database – Frequency of the concept words in the database – Non-linear formula (QUESTEL Lingway trade secret) – Normalized to 100 (high score)
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