USPTO Issues CDD Patent

CDD has been awarded a U.S. utility patent for a "Hybrid machine-user learning system and process for identifying, accurately selecting and storing scientific data" (U.S. Patent No. 9,594,743, issued March 14, 2017).

From the desk of Mark A. Harper, Ph.D., Partner, Dinsmore & Shohl LLP, Legal Counsel (CDD's Patent Lawyer)

The United States Patent and Trademark Office (USPTO) officially issued U.S. Patent No. 9,594,743 assigned to CDD on March 14, 2017.  The CDD patent is directed to a hybrid machine-user learning system and process for identifying, accurately selecting and storing scientific data. This includes use in screening runs measuring drug candidates or medical knowledge bases that go into more detail about the biological systems being assayed.  After examination of the application by the USPTO, which included a review of the application, a search of prior art and a comparison of the claims of the application with the prior art uncovered during the search, the USPTO noted that the prior art “does not disclose, teach or suggest” the invention as recited in the claims.  Particularly, the USPTO noted that the scoring and ranking of possible annotations assigned to Part of Speech (POS)-tagged blocks as taught in the application and recited in the claims was novel and not obvious in view of the prior art.

The full patent abstract, images, description, and claims can be cleanly viewed in Google Patents here:

https://www.google.com/patents/US9594743

For more information on the new BAE product using this patented technology to annotate your assays more efficiently in machine-readable, semantically-marked up formats, please see the BioAssay Express (BAE) website or schedule a free demonstration via email to sales@collaborativedrug.com.

This blog is authored by members of the CDD Vault community. CDD Vault is a hosted drug discovery informatics platform that securely manages both private and external biological and chemical data. It provides core functionality including chemical registration, structure activity relationship, chemical inventory, and electronic lab notebook capabilities.

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