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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Search Tool Tidbits: Scholrly, Clustify, and HQ Books

In the last few posts on the Intellogist Blog, we’ve focused on major updates to large patent search tools, such as Google Patents and TotalPatent.  Today I’ll take a step back from the big players and highlight some obscure search tools that may give you that extra boost you need to locate that one relevant piece of non-patent literature (NPL) prior art which you’d have otherwise overlooked. Scholrly is free search engine for academic papers (currently in closed beta-testing phase) that may one day rival Google Scholar, and HQ Books is a free PDF search tool which can help you locate product manuals and user guides from all over the world.  For those patent analysts who want their daily dose of obscure resources: don’t worry, I have one for you, too!  We’ll take a quick look at Clustify, document clustering software that identifies important keywords, representative documents, and a hierarchy of customized tags for almost any dataset.

Continue reading for a round-up of little-known tools for prior art searchers and data analysts!

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