Paper Info
Title
Large-scale, parallel automatic patent annotation
Abstract
When researching new product ideas or filing new patents, inventors need to retrieve all relevant pre-existing know-how and/or to exploit and enforce patents in their technological domain. However, this process is hindered by lack of richer metadata, which if present, would allow more powerful concept-based search to complement the current keyword-based approach. This paper presents our approach to automatic patent enrichment, tested in large-scale, parallel experiments on USPTO and EPO documents. It starts by defining the metadata annotation task and examines its challenges. The text analysis tools are presented next, including details on automatic annotation of sections, references and measurements. The key challenges encountered were dealing with ambiguities and errors in the data; creation and maintenance of large, domain-independent dictionaries; and building an efficient, robust patent analysis pipeline, capable of dealing with terabytes of data. The accuracy of automatically created metadata is evaluated against a human-annotated gold standard, with results of over 90% on most annotation types.
Year
DOI
Venue
2008
10.1145/1458572.1458574
PaIR
Keywords
Field
DocType
text analysis tool,automatic patent enrichment,parallel automatic patent annotation,richer metadata,new product idea,metadata annotation task,robust patent analysis pipeline,automatic annotation,current keyword-based approach,new patent,annotation type,gold standard,text analysis,parallel,gate,information extraction
Metadata,Annotation,Information retrieval,Terabyte,Computer science,Image retrieval,Exploit,Information extraction,Patent analysis,New product development
Conference
Citations 
PageRank 
References 
7
0.70
5
Authors
8
Name
Order
Citations
PageRank
Milan Agatonovic11758.08
Niraj Aswani218911.21
Kalina Bontcheva32538211.33
Hamish Cunningham42426255.41
Thomas Heitz570.70
Yaoyong Li639326.55
Ian Roberts720717.68
Valentin Tablan81359119.57