Abstract
A patent is a legal right given to novel, non-obvious and useful inventions. The prior-art search involves retrieving prior works related to it to avoid duplication of the invention and granting of the patent. Moreover, it analyzes a variety of documents like newspaper articles, proceedings, and journals. The amount of patent document and the volume of filings keep on increasing at an unprecedented rate every year. Processing on this enormous volume of data sequentially is time-consuming. Hence, the proposed Prior-Art Retrieval System (PARS) retrieves only the patent documents through Google patent API, and K-Means clustering was employed in a parallel mode to cluster the documents. Through Relevance Mapping prominent document clusters were identified. The documents within the relevant clusters are ranked based on the citations. The top ranked documents were displayed to the patent analyst.The results show that the processing time with map reduce has reduced significantly and accuracy of clusters was around 50%.
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Girthana, K., Swamynathan, S. (2018). Efficient Prior-Art Retrieval of Patent Documents Using MapReduce Paradigm. In: Mandal, J., Saha, G., Kandar, D., Maji, A. (eds) Proceedings of the International Conference on Computing and Communication Systems. Lecture Notes in Networks and Systems, vol 24. Springer, Singapore. https://doi.org/10.1007/978-981-10-6890-4_70
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DOI: https://doi.org/10.1007/978-981-10-6890-4_70
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