Journal Article10.1007/S00500-013-1059-X
Patent value analysis using support vector machines
Secil Ercan,Gulgun Kayakutlu +1 more
- 01 Feb 2014
- Vol. 18, Iss: 2, pp 313-328
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TL;DR: The proposed model in this study will help the decision makers to predict whether the patent appeal will be accepted, and is unique with the approach that helps the candidate patent owners.
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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.
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Citations
A patent quality analysis and classification system using self-organizing maps with support vector machine
Jheng-Long Wu,Pei-Chann Chang,Cheng-Chin Tsao,Chin-Yuan Fan +3 more
- 01 Apr 2016
TL;DR: The proposed SOM-KPCA-SVM is applied to classify patent quality automatically in patent data of the thin film solar cell and experimental results show that the proposed system can capture the analysis effectively compared with traditional manpower approach.
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TL;DR: In this article, the patent citation network is modeled as a discrete time, discrete space stochastic dynamic system, and an attractiveness function, called A(k,l)$, which determines the likelihood that a patent will be cited is extracted from more than 2 million patents and their citations.
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A transferability evaluation model for intellectual property
TL;DR: This study is one of the first to quantitatively evaluate patents in terms of transferability and, thus, the proposed model can be used for valuing patents and distinguishing quality patents that are marketable.
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Relative density degree induced boundary detection for one-class SVM
Fa Zhu,Jian Yang,Sheng Xu,Cong Gao,Ning Ye,Tongming Yin +5 more
- 01 Nov 2016
TL;DR: Experimental results show that merely preserving about 20 % of the training set, the performance will not decrease and be better than previous related method, but the model is simpler and the training process is faster.
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Strategies for managing intellectual property value: A systematic review
TL;DR: In this paper, a review of 168 journal articles on IP value, identifying main research concepts and strategies for managing IP value is presented, using insights from the review, the study provides a multi-level framework describing the dominant logic and key factors for managing the IP value and provides recommendations on some potential future research directions for studying IP value.
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