Abstract
In the era of knowledge economy and industry 4.0, global production costs for products have become increasingly competitive, how companies obtain benefits of patent commercialization by developing key technologies to make applications for related patents becomes very important. However, a large quantity of patent information requires much time for enterprises to identify patent values. Traditional evaluation methods of patent quality rely on experts to determine patent values, which requires long time spent and ends with subjective assessment results. Therefore, the research proposes a quantitative evaluation method of patent quality by constructing a Python-based system for patent quality classification to research on patent evaluations. Evaluation of patent values by taking patent indicators into consideration helps explore what kind of patents are fundamental and essential for enterprises. Patent evaluations help managers and R&D personnel understand patent values and identify related business opportunities, which further determines the directions of patented technology development. The process for the research method is divided into three parts. The first part is patent retrieval and patent indicators collections. Second part introduces Principal Component Analysis (PCA) to make key patent indicators selection. Third part ends with the Deep Neural Networks (DNN) analysis result. The research takes Internet of Things (IoT) applied in manufacturing industries as the example case, and find out high-value patents by using DNN to classify patent quality. Moreover, the research puts many efforts into both worldwide high-value patents and those of Taiwan to explore their portfolios, which provides useful suggestions for Taiwan assignees in the R&D strategies of IoT technologies.