All tagged 35 USC 102

Using Deep Neural Networks to Strengthen Your Information Disclosure Statement Submissions

Using Deep Neural Networks to Strengthen Your Information Disclosure Statement Submissions

James Busch
Deep Neural Networks, Patent Prosecution

With more comprehensive art citations during prosecution, a patent will be more likely withstand attacks under 35 USC 102 and 35 USC 103. 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 for consideration. 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 immunized against attack from these or similar references.

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

James Busch
Deep Neural Networks, Due Diligence

Deep neural network sentence encoders can be a very powerful tool to help you automate the process of reviewing and applying an inhuman number of potential invalidating references against a set of subject patent claims. After a search is conducted, the sentences and paragraphs of the resulting patents and publications can be automatically compared against the specific claim language of the subject patent. A full analysis of the search results may indicate even stronger patents or combinations of patents to apply against a subject patent claims than the art of record, or may help to confirm the strength of the subject patent claims relative to a larger collection of art references than originally cited during prosecution.

Using Deep Neural Networks to Find the “Best” Known Cited Art and Create Claim Charts

Using Deep Neural Networks to Find the “Best” Known Cited Art and Create Claim Charts

James Busch
Deep Neural Networks, Due Diligence

When initially reviewing a patent portfolio to understand its scope, it is helpful to understand the universe of art that has already known in the portfolio. When there are a large number of known art references, it is extremely helpful to be able to focus on the "best" art that is of record. Using deep neural network sentence encoders, we can automate the discovery of the "best" parts of the known art.

What do Contingent Fee Lawyers Consider when Evaluating Novelty and Non-Obviousness?

What do Contingent Fee Lawyers Consider when Evaluating Novelty and Non-Obviousness?

James Busch
Patent Due Diligence

Patent lawyers must consider the question of novelty and obviousness when considering a case on a contingent fee basis. Lack of novelty due to anticipation is rarely a dispositive factor during a contingent fee licensing program.  Typically the more significant issue becomes combinations of art with "expert" arguments that "it would have been obvious" to come up with the invention.  However, with knowledge of the law, inventors can take action to establish a factual record of objective evidence of non-obviousness throughout the inventive process to combat future “obviousness” rejections.