James R. Love
November 1, 2021
Drafting claims with infringement in mind has always been a challenge. For instance, claims should be drafted to ensure that they can be infringed by a single party in order to address divided infringement issues. Similarly, it may be useful to draft claims in a way that avoids requiring end-user infringement as end-users may not be the best target when considering litigation. In the same vein, it is important to draft claims in a way that infringement is detectable, as a patent owner must have facts that provide a plausible entitlement to relief. This means that the patent owner must have some basis to allege that the patent claims are being infringed. If the claims include features that are not readably detectable, the claims may, in effect, be useless when considering infringement. This is especially true in Artificial Intelligence where many features are difficult to easily detect without intimate knowledge of the AI system. In fact, AI systems are often considered black box systems in which the inner workings are not evident to the outside and sometimes not even to the operator of the AI system.<... Read more
Kurt M. Berger, Ph.D.
October 18, 2021
As previously discussed in this blog (see Creation vs. Conception: Can an AI Machines be an Inventor?) some AI researchers believe that the output of certain sophisticated AI software can represent a new invention conceived of, not by the author, user, or trainer of the software, but by the software itself, and that the software or the “AI machine” should be listed as the inventor on a patent application for the new invention. This AI-machine-as-inventor proposition is currently being tested by Dr. Stephen Thaler via the filing of patent applications in over a dozen countries for inventions allegedly invented by a neural-network-based AI machine (“DABUS”), and the listing of the inventor as “DABUS” on the application (with Dr. Thaler listed as the applicant and assignee). At the time of our previous posts last fall, the USPTO, the EPO, and the UKIPO each objected to listing DABUS as the inventor since DABUS is not a natural person.<... Read more
October 7, 2021
by: Robert W. Downs, Ph.D.
My career includes work in research in artificial intelligence, patent examination at the U.S. Patent and Trademark Office, and patent application preparation and prosecution, all over a period of about 40 years. Over that period, I have completed three Masters Degree programs in computer-related fields. As a researcher, I have experienced the difficult challenge of not just writing computer programs in programming languages such as Fortran and Lisp, but getting the programs to work and actually produce desired results. As a patent examiner, I have experienced the difficult challenge of searching for prior art based on a high level description of an invention, as well as judging whether a computer-related invention is patentable under Section 101. As a patent agent, I have experienced a difficult challenge of preparing patent application for computer-related inventions, while keeping in mind a broad range of potential prior art and the high possibility of receiving a rejection under Section 101.<... Read more
Michael R. Casey, Ph.D.
August 27, 2021
As a follow up to the June 11, 2021 post entitled “Application of AI in Legal Services,” this post examines the use of artificial intelligence (AI) in an additional legal area -- eDiscovery in litigation. One of the many time-consuming tasks in litigation is the review of documents to assess whether (a) a document is relevant to any outstanding discovery request or mandatory disclosure, and, if so, (b) if the document is covered by at least one factor that would prevent its disclosure (e.g., due to attorney work product, attorney-client privilege, spousal-privilege, and/or another privilege). Furthermore, document review often is performed with an eye toward flagging certain documents as being important to an issue in the case, either as part of a defense or as part of a party’s case in chief. <... Read more
August 2, 2021
On July 15, a team of scientists published a Nature article, titled “Highly accurate protein structure prediction with AlphaFold.” The article describes how the neural network model developed by Google’s DeepMind can predict protein structures “with atomic accuracy even where no similar structure is known.” In addition, DeepMind has now open-sourced the code for AlphaFold 2, allowing further collaborations for even more accurate protein structure prediction.<... Read more