A patent examining expert system using pattern recognition

To know whether a patent is registered or rejected, one should rely on the subjective judgments of patent examiners and patent attorneys. In order to overcome this drawback, we propose an algorithm which is able to automatically examine the registration of a patent depending on objective patent data. In this paper, we create the proposed algorithm as a system composed of three procedures: Weight Value Selection, Rejection Criterion Value Selection, and Prediction. In Weight Value Selection, the core words which are the main content of patent documents are extracted. The algorithm finds the average word appearance rates in the document and compares it to the average number of words from rejected patent documents. The algorithm extracts core words from the patent documents and integrates them into an integration core word database. In Rejection Criterion Value Selection, the algorithm extracts the core words from other patent documents that are not used for generating the integration core word database. It finds a relevant document's similarity value using extracted core words and the weights of the integration core word database. After that, the algorithm sets each document's similarity value and accepts the result about the registration or rejection of each patent document as an input value. The algorithm sets the boundary value of a class by running pattern recognition algorithms such as K-means. Perceptron, and Regularized Discriminant Analysis. In the third procedure, Prediction, the algorithm extracts the core words from patent documents for prediction. The algorithm compares the two values created in the first and second steps, and uses a similarity value to predict acceptance or rejection. The proposed Automated Patent Examining System in this paper derives objective prediction results based on past patent data so that we do not have to rely on the subjective judgments of the existing group of patent examiners and patent attorneys to know if a patent will be registered. (C) 2010 Elsevier Ltd. All rights reserved.