written by The Law Offices of James David Busch LLC. 

James is Licensed to Practice before the USPTO, in Illinois and Arizona.

Using Deep Neural Networks to Strengthen Your Information Disclosure Statement Submissions

Using Deep Neural Networks to Strengthen Your Information Disclosure Statement Submissions

In my prior article “Validity and the Duty of Candor”, I discuss the importance of filing Information Disclosure Statements during prosecution of patent applications for satisfying the duty of candor under 37 CFR 1.56.

The benefits of comprehensive art citations also can help include strengthening the patent against the prior art:

  • establishing the state of the art at the time of the invention, and

  • strengthening the resulting issued patent if issued issue in view of and with consideration of the art.

In other words, with more comprehensive art citations during prosecution, a patent will be more likely to withstand attacks under 35 USC 102 and 35 USC 103. That is, so long as these citations are helpful to the Examiner, and the public (and not merely overwhelming).

This benefit to the validity of a patent can be maximized when the most relevant portions of each cited reference are made of record and presented to the Examiner, and the public, for consideration during examination. This can be done by utilizing pin citations to the most relevant portions of the art.

With deep neural network sentence encoders, highly detailed and relevant pin citations for each art reference can be mapped to each claim element sought during prosecution and provided to the Examiner for consideration. Any resulting patent will be, arguably, immunized against attack from these or similar references.

To the extent a detailed art search has already been conducted, the deep neural network algorithm can be run against the known art. See Using Deep Neural Networks to Find the “Best” Known Cited Art and Create Claim Charts. Optionally, a broad search for new potentially prior art can be conducted, and the deep neural network algorithm can be run against the new art. See Using Deep Neural Networks to Find Highly Relevant Unknown Prior Patents and Create Invalidity Charts.

Once complete, the Examiner can be provided with helpful information pertaining to the cited art’s relevance to the specific claims:

  • This pin-cite information can be included on USPTO Form SB08 in the “Pages, Columns, Lines where Relevant Passages or Relevant Figures Appear” text box.

  • A ranked list of the patents, like those presented in my prior articles above, can be presented as part of the IDS Remarks to help focus the Examiner on the patents that are believed to be the most relevant to the particular claims pending in the application.

  • Optionally, claim validity / invalidity charts can be automatically generated, manually reviewed, and submitted as part of the IDS Remarks so that they become part of the public record and are considered during prosecution by the Examiner.

To the extent that the patent issues over the art, and the detailed citations, the patent will be less likely to succumb to a 35 USC 102 or 35 USC 103 challenge in view of those (or similar) patents in future litigation or post-grant proceedings.

If you would like to explore this process further, contact JDB IP.

Using Deep Neural Networks to Mine Your Patent Specification for Claims that Your Competitors Want

Using Deep Neural Networks to Mine Your Patent Specification for Claims that Your Competitors Want

Using Deep Neural Networks to Find Highly Relevant Unknown Prior Patents and Create Invalidity Charts

Using Deep Neural Networks to Find Highly Relevant Unknown Prior Patents and Create Invalidity Charts