Google Acquires IBM Patents, Many Focused On Hardware
Google acquired about 1,022 patents from IBM in a deal that follows the purchase of about 1,000 IBM patents in July.
The range of inventions varies from desktop to circuit design, parallel database systems and architecture to user authentication and smartcard testing, according to SEO by the Sea founder Bill Slawski.
"Google also has been buying up smaller patent portfolios and many seem focused more on hardware than software," he said. "The patents I found today look like they originated from patent investment companies and portfolio holders."
But Google isn't only acquiring patents, it's also selling them off. Last week, Google sold nine patents to HTC to help them pursue a patent infringement lawsuit against Apple.
Separately, Google last week updated a filing for a patent that would provide additional filters on searches, perhaps for vertical search. The Unified Search Interface describes a method to sort and group results. It would provide "search modes, which correspond to specific subjects, are automatically suggested to the user based on the search query provided, without requiring the user to know and select the appropriate search mode before providing the search query." The original patent filed in January 2011.
Google General Council Ken Walker explained earlier this year in a blog post the company's position on patents and patent reform, before Google made a bid for the Nortel portfolio, which it failed to acquire. "The patent system should reward those who create the most useful innovations for society, not those who stake bogus claims or file dubious lawsuits," he wrote.
But Google isn't the only search engine company acquiring patents from IBM.
Slawski said Yahoo acquired about 50 patents in the early 2000s. A Yahoo patent filed in June 2009, and updated Tuesday in the United States Patent and Trademark library describes a system and method that provides each page collected with search success metrics based on machine learning. A series of steps predict the target page success metrics associated with each subset of the search engine results before being evaluated.
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