From the Complete Article:
The Patent and Trademark Office has teamed up with Cisco Systems Inc., Alphabet Inc.’s Google, and the Massachusetts Institute of Technology to create an online archive about inventions aimed at boosting U.S. patent quality.
The “Prior Art Archive,” hosted by MIT’s Media Lab, is open to patent office examiners and anyone who might want to submit published writing, patents, information about products in commercial use, and other materials, known as prior art, Cisco and MIT said.
Agency director Andrei Iancu has focused on improving patent quality since taking the reins last February. Patent examiners search prior art to ensure inventions described in patent applications are novel enough to warrant a patent.
“By collecting various non-patent literature in one database, and making it searchable and readily available to the entire industry, the Prior Art Archive will help our examiners, and the public, to identify additional prior art upfront, during the examination process,” Iancu said in an emailed statement. “This will help increase the quality of examination and the reliability of the patent grant.”
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From a Google Blog Post:
We’re proud to support the Prior Art Archive, and have devoted significant resources to this and other important quality initiatives. The Prior Art Archive is searchable through Google Patents, and all of the documents in the Archive have been labeled with Cooperative Patent Classification codes using Google’s machine learning models. The labels are a feature we rolled out in Google Patents to help make the most relevant technical materials easier to find. We’ve also recently launched a site accessible to the public and examiners, TDCommons, where companies can publish technical information they don’t want to patent free of charge.
We’re also excited to use AI and machine learning to take prior art searching to the next level. To this end, we’ve recently created an open ecosystem, the Google Patents Public Datasets, to make large datasets available for empirical public policy, economics, and machine learning research. We’re committed to developing and making available technology that improves patent quality, and ultimately strengthens our patent system.
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