Multi-classification of Patent Applications with Winnow

Koster, C. H. A.; Seutter, Marc; Beney, Jean

doi:10.1007/978-3-540-39866-0_53

Cited by 25 publications

(29 citation statements)

References 9 publications

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“…The average of category for document was 36, and the total of categories was up to 426. The results by the K Gra kernel proposed approach yielded 86.67% accuracy overcome the 81% of manually processing and the results of previous work (Koster et al, 2003). In the following, we describe a slightly modified Probabilistic Neural Network (PNN) used to solve the optimization problem of text categorization.…”

Section: Related Workmentioning

confidence: 89%

Particle Swarm Optimization Applied to Parameters Learning of Probabilistic Neural Networks for Classification of Economic Activities

Ciarelli¹,

Krohling²,

Oliveira³

2009

Particle Swarm Optimization

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Section: Related Workmentioning

confidence: 89%

Particle Swarm Optimization Applied to Parameters Learning of Probabilistic Neural Networks for Classification of Economic Activities

Ciarelli¹,

Krohling²,

Oliveira³

2009

Particle Swarm Optimization

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“…Automatic patent classification based on supervised and unsupervised machine learning algorithms has been studied for over a decade [12][13][14][15][16] As all machine learning algorithms need a formal representation of the document, a feature selection phase is essential before classification. In this phase, some terms of a document are selected to build a feature space on which the classification algorithms can work.…”

Section: Automatic Patent Classificationmentioning

confidence: 99%

A Supervised Requirement-oriented Patent Classification Scheme Based on the Combination of Metadata and Citation Information

Zhu¹,

Wang²,

Zhu³

et al. 2015

IJCIS

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Patent classification systems are applied extensively in innovative analysis. Existing patent classification schemes are either technology-dependent or TRIZ-based. The former ones, such as the IPC and UPC, are normally developed by different patent offices in the world mainly for the purpose of patentability examination and patent retrieval, while the latter is for TRIZ users and analysts with no more than 40 categories. These static classifications are too complex and general to meet the in-depth patent classification requirements of a specific technology area or organization. To tackle these drawbacks, in this paper, we propose an automatic requirement-oriented patent classification scheme as a complementary method using supervised machine learning techniques to classify patent dataset into a user-defined taxonomy. The requirement-oriented patent taxonomy can be technology-dependent, application-dependent or a mixture of both tailored to specific business objectives. It is more comprehensible and adaptable to various patent management requirements. Through a set of experiments on a collection of 14,414 patents in a case study in the technology area of system on a chip (SoC), we recommend using the combination of the metadata and citation information as the document representation for the new method since it can obtain relatively high classification accuracy with a dramatically simplified document preprocessing process.

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“…For patent offices, the primary classification task is the association of International Patent Classification codes as well as national or European classifications. These classification systems are too fine to realistically achieve sufficiently high accuracy by automated classifiers so some efforts have focused on using them for the preclassification stage, where patent applications are associated to the appropriate technical unit for the examination phase [4,5]. Texts of patents are widely and freely available on the world wide web, making patents ideal subjects for automated classification.…”

Section: The Development Of Automated Classification Tools In the Arementioning

confidence: 99%

“…Texts of patents are widely and freely available on the world wide web, making patents ideal subjects for automated classification. Consequently, various publications have described attempts of automated patent classification over the last years [2,4,5,6,15,18,19]. Most of these have originated from patent offices, possibly due to the confidentiality that is often applied by commercial organisations with regards to their research .…”

Section: The Development Of Automated Classification Tools In the Arementioning

confidence: 99%

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The impact of metadata on the accuracy of automated patent classification

Richter¹,

MacFarlane²

2005

World Patent Information

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractDuring the last decade, the advance of machine learning tools and algorithms has resulted in tremendous progress in the automated classification of documents. However, many classifiers base their classification decisions solely on document text and ignore metadata (such as authors, publication date, and author affiliation). In this project, automated classifiers using the k-Nearest Neighbour algorithm were developed for the classification of patents into two different classification systems. Those using metadata (in this case inventor names, applicant names and International Patent Classification codes) were compared with those ignoring it. The use of metadata could significantly improve the classification of patents with one classification system, improving classification accuracy from 70.8 up to 75.4 percent, which was highly statistically significant. However, the results for the other classification system were inconclusive:while metadata could improve the quality of the classifier for some experiments (recall increased from 66.0 to 68.9 percent, which was a small but nonetheless significant improvement), experiments with different parameters showed that it could also lead to a deterioration of quality (recall dropping as low as 61.0%). The study shows that metadata can play an extremely useful role in the classification of patents. Nonetheless, it must not be used indiscriminately but only after careful evaluation of its usefulness.

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Multi-classification of Patent Applications with Winnow

Cited by 25 publications

References 9 publications

Particle Swarm Optimization Applied to Parameters Learning of Probabilistic Neural Networks for Classification of Economic Activities

Particle Swarm Optimization Applied to Parameters Learning of Probabilistic Neural Networks for Classification of Economic Activities

A Supervised Requirement-oriented Patent Classification Scheme Based on the Combination of Metadata and Citation Information

The impact of metadata on the accuracy of automated patent classification

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