Logistic Regression Analysis of Patent Approval Rate in Sweden

Detta är en Kandidat-uppsats från KTH/Skolan för teknikvetenskap (SCI); KTH/Skolan för teknikvetenskap (SCI)

Författare: Amelia Lindroth Henriksson; Simon Koller; [2018]

Nyckelord: ;

Sammanfattning: This thesis was conducted to investigate what factors impact the outcome of a patent application for the Swedish market. The method used was logistic regression and the data was extracted from the database of The Swedish Patent and Registration Offi ce, PRV. The analysis in this thesis started with 47 covariates, including the 35 IPO technical fields, resulting in a model consisting of five covariates. The most important covariates were determined to be the number of notices issued by PRV, whether or not a patent attorney was used and applicant type. The number of notices had a positive impact on the probability of the success of a patent application. Being a company and hiring a patent attorney also increase the chances of the patent being granted. The derived final model showed a high predictive ability and provides insight of significant factors of a successful patent application.

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