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 keywordbased 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.

Converting and Annotating Quantitative Data Tables

“…However, such an annotation system needs to tackle the ambiguity problem if it is to be succesfully used in the domain of quantities and units. We know of two existing annotation systems that target the domain of quantities and units [7,1], and our research can be seen as a continuation of these efforts. The results of these systems are good (over 90% F-measure), but they target "clean" datasets such as patent specifications, or focus on part of the total problem, such as detecting units only.…”

GATE Teamware: a web-based, collaborative text annotation framework

“…GAS provides a straightforward mechanism for running applications, created with the GATE framework, as web services that carry out various NLP tasks. In practical applications we have tested a wide range of services such as named entity recognition (based on the freely-available ANNIE system Cunningham et al 2002), ontology population (Maynard et al 2009), patent processing (Agatonovic et al 2008), and automatic adjudication of multiple annotation layers in corpora.…”

GATECloud.net: a platform for large-scale, open-source text processing on the cloud

“…Recent projects have increasingly faced the problem of running GATE-based text processing on terabyte datasets [21]. At the same time, the multi-tier service-oriented architecture of GATE Teamware, coupled with its centralized workflow engine, has made its deployment and administration too complex and error prone for many researchers.…”

“…Additional experiments, not detailed here owing to space constraints, showed that processing time scales linearly with the number of tweets, on The news and Twitter datasets were annotated for named entities with the standard ANNIE entity annotation pipeline [11], deployed as SaaS within GATECloud.net. For the patents dataset, we reused a pre-existing text-processing pipeline [21] that recognizes patent-specific types, including references to other patents, scientific publications, measurement expressions, patent sections, claims, examples, references to figures and tables.…”