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Soft Computing
Erschienen in: Soft Computing 2/2014

01.02.2014 | Methodologies and Application

Patent value analysis using support vector machines

verfasst von: Secil Ercan, Gulgun Kayakutlu

Erschienen in: Soft Computing | Ausgabe 2/2014

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Abstract

Receiving patents or licenses is an inevitable act of research in order to protect new ideas leading innovation. Request for patents has increased exponentially in order to legalize the intellectual property. Measuring economical value of each patent has been widely studied in the literature. Majority of the research in this field is focused on the patent driver prospect handled for the patent offices. There are a variety of criteria affecting decisions on each patent right; and predicting the possibility of grant may help the researchers to take some precautions. Objective of this study is to propose a robust model to determine if the appeal has a chance of approval. A case study is run on the patents that are accepted and rejected in home appliance industry to construct an intelligent classification model. The support vector machine, Back-Propagation Network and Bayes classification methods are compared on the proposed model. The proposed model in this study will help the decision makers to predict whether the patent appeal will be accepted. The study is unique with the approach that helps the candidate patent owners.
Vorheriger Artikel Applying FARSJUM intelligent system to derive priorities in sparse hierarchical problems
Nächster Artikel A comparative analysis of evolutionary and memetic algorithms for community detection from signed social networks

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Metadaten
Titel
Patent value analysis using support vector machines
verfasst von
Secil Ercan
Gulgun Kayakutlu
Publikationsdatum
01.02.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 2/2014
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-013-1059-x

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