A Feasible Dashboard to predict Patent Mining Using Classification Algorithms

Abstract

The number of patents that are being filed across the world is increasing day by day. With the increase in patents being filed the process of segregating the patents based on their class becomes even more difficult. There are a set of features that are extracted from the dataset that is previously present. The features that are being extracted will vary for each document. Patents documents are issued as legal rights by the government for protecting the owner of invention. This exclusive right helps in protecting the invention to be used by others from using, selling, developing another, etc for some period of time. After the feature extraction is done there are two steps that need to be carried out, namely: Classification and prediction. For this purpose, decision tree algorithm is used which makes use of the most prominent feature and classification is done using those features. Therefore, for classification a hierarchical decision tree algorithm is used along with the probability of patent conversion. Based on the classification that is done a model will be created and whenever a new entity is brought it is compared with the model file that was created using the available datasets and is predicted as a particular class. Thus, both classification of existing dataset and the prediction for any new dataset based on previous inputs can be achieved thereby facilitating the patent mining process In proposed system this paper mainly concentrating on the prediction of the patent using data mining techniques. Decision tree algorithm for the classification of the patent mining is applied. And also classification of the data using some attributes is done, namely; association frequency, grant or applicant, patent country, patent status, grant patent using these attributes prediction of patent mining using Naive Bayes algorithm is done. The proposed algorithm gives best result for patent mining. The patent status is checked whether he got grant access of the patent in prediction which is the best patent mining in all over the country. The graph is plotted for to find the accuracy. Based on this if the new tuple is add to dataset it has to classify in which patent domain it belongs to.