Semantic searches can be effective and time-saving in prior art searches, even when prior art is intentionally vague or unclear. It is handy when a researcher has vast knowledge about the domain and market usability. In semantic searching, what is lost in terms of transparency and control, is gained in terms of the intrinsic relationship extracted between the concept and the documents.
Semantic search combines a research professional’s knowledge with an intelligent algorithm that can learn how to break complex queries into manageable chunks and decode the correct meaning. There are several prior art analysis tools available in third-party search databases. The semantic searching performed using these tools is an efficient and easy way to access relevant prior arts.
Further, the tool's selection can be based on the relevance and the limit of the results provided by the database, making it more convenient for the researcher to zero down on the final list of potentially relevant prior arts.
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