Meeting the Challenges of Data Collection and Analysis

As a leader in patent analysis, Landon IP has expert searchers who answer complex questions on a daily basis to produce the highest quality results. If you work with patents, you know the obstacles we face: data collections are huge and unwieldy; errors in the data are rampant; and in short, nothing involving patent data is ever easy. Today, I’m going to share some of our most basic strategies for producing high-quality datasets that lead to reliable results. Read on to get an insight into these best practices.

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Should a Single-Publisher Platform be Called a “Database”?

Another interesting discussion recently took place on the CHMINF listserv that tried to differentiate between publisher compilations and databases.  This topic is very relevant to patent searchers, since we are constantly trying to locate comprehensive, reliable sources of non-patent scientific literature that can be used as prior art.  If a patent searcher believes that they are accessing a comprehensive database that covers a wide range of materials, and the database is actually limited to only journal articles and books produced by a single publisher, then the searcher may overlook crucial non-patent literature (NPL) prior art if they focus the search on that one database.  A patent searcher should always look at multiple databases so as not to overlook any relevant NPL.  It makes the searcher’s job easier to access a platform that covers a wide range of publishers and journal titles, instead of only works by a single publisher.

After the jump, we’ll look at some of the highlights from the debate on the CHMINF listserv, try to define a database and a publisher compilation, and look at some examples of these single-publisher platforms.
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