Abstract :
As an important operation for finding existing relevant patents and validating a new patent application, patent search has attracted considerable attention recently. However, many users have limited knowledge about the underlying patents, and they have to use a try-and-see approach to repeatedly issue different queries and check answers, which is a very tedious process. To address this problem, in this paper, we propose a new user-friendly patent search paradigm, which can help users find relevant patents more easily and improve user search experience. We propose three effective techniques, error correction, topic-based query suggestion, and query expansion, to improve the usability of patent search. We also study how to efficiently find relevant answers from a large collection of patents. We first partition patents into small partitions based to their topics and classes. Then, given a query, we find highly relevant partitions and answer the query in each of such highly relevant partitions. Finally, we combine the answers of each partition and generate top-k answers of the patent-search query.
Keywords :
human computer interaction; patents; query processing; relevance feedback; error correction; highly relevant partitions; large patent collection; patent application; patent search query; patent search usability improvement; query answering; query expansion; relevant patent search; top-k answer generation; topic-based query suggestion; try-and-see approach; user search experience improvement; user-friendly patent search paradigm; Computational modeling; Context; Equations; Indexes; Mathematical model; Patents; Search problems; Patent search; error correction; query expansion; query suggestion;
