2020 October

This article first appeared in the AIPLA Patent Law Committee October 2020 Newsletter.

By Hayley Talbert and Michael M. Morris

For the past few years, the USPTO has been quietly testing a way to automatically import prior art references from related applications at the USPTO (i.e., without any prompt from the Applicant) and improve examiner review of those references.  The program, referred to as the Access to Relevant Prior Art (RPA) initiative, launched in November of 2018 and is currently being piloted in one Art Unit in each of the nine technology centers at the USPTO (currently Art Units 2133, 1616, 1731, 2431, 2675, 2879, 2922, 3635, and 3753). Other than a recent announcement regarding the Art Units within the program and the occasional Notice of Imported Citations received by an Applicant—which indicates their application was entered in the program—the USPTO has been relatively silent regarding this initiative. So, earlier this summer, AIPLA Patent Law Committee Members Hayley Talbert and Mike Morris met with USPTO personnel working on this program to understand how the initiative has been going and learn where the initiative may go. The following is a summary of what the USPTO shared with the AIPLA.

The RPA initiative is currently in Phase 1, which is limited to US patent applications with only one non-provisional parent application or patent and in only certain Art Units.  For applications in the initiative, software created to implement the RPA initiative collects prior art from Information Disclosure Statements (IDSs), PTO 892 forms, and Third Party Submissions in the parent application or patent and presents those references to the Examiner as a Master Reference List.  An applicant whose application is included in the RPA initiative receives a Notice of Imported Citations informing them of their application’s inclusion and listing the references on the Master Reference List.  The Applicant, however, need not take any affirmative steps to participate in this program.

Currently, for citations printed on the face of a patent outside of the RPA, the references come from up to three sources (Applicant provided, Examiner cited, Third Party Submission). Under the RPA, the USPTO has now added another category—automatically imported references.  All four of these categories of prior art references will be printed on the face of patent granted from the RPA.

So why was this program created? The USPTO’s primary goals in the program are to increase patent examination quality and efficiency. An automated tool developed for USPTO examiners to help them review the references from the Master Reference List is the primary means to achieve these goals.  While the USPTO is still collecting data on the new tool which is currently available only for cases in the RPA, the general sense is the new automated tool for examiners has increased the number of relevant reference presented to the Examiner and also does aid the Examiner in his/her review of those references.

Another goal of the RPA is to assist Applicants and patent prosecutors in meeting the duty of disclosure under 37 C.F.R. 1.56, and—hopefully—save Applicant’s money during prosecution. Whether this goal is being met is unknown. From informal conversations, many practitioners seem unfamiliar with the program and, therefore, may be surprised when they receive a Notice of Imported Citations—perhaps causing them to spend time investigating the program. Additionally, while some practitioners that receive a Notice of Imported Citations may trust the completeness of the references listed, the AIPLA suspects many practitioners will compare the list of references on the Notice of Imported Citations with the references in their own records of prior art to ensure no relevant reference is omitted.

So what are the USPTO’s plans for future phases of the RPA? While the RPA currently imports references from only an immediate parent application or patent, the USPTO intends to eventually pull references from more distant family members (grandparents, PCTs, etc.). Also, rather than the single import done now, the USPTO plans to import relevant prior art in real-time (e.g., as citations are entered in the parent case). Additionally, the USPTO intends to collect prior art from other patent offices (e.g., the EPO and JPO).

While Phase 1 is about to have its second anniversary, the USPTO has not yet scheduled a transition to Phase 2 of the initiative.  When Phase 2 occurs, however, practitioners may see an increase in the number of Examiners using the Master Reference List and related new tools, expansion in the types of applications allowed in to the initiative (such as applications that have more than one priority claim), and expansion in the sources from which the prior art references are collected. The USPTO is also considering providing a mechanism for Applicants to indicate other applications to be imported, such as cross-cited families that may not share a priority claim.

Takeaway, the USPTO is currently automatically importing references for only certain cases in certain Art Units. If you receive a Notice of Imported Citations, please know that the USPTO is currently doing only a one-time import of references from the parent application.  If the parent application is still pending and additional prior art arises in the parent application after the import into the child application, practitioners will need to identify and provide such references to the USPTO in an IDS if they want such references to be considered by the examiner and listed on the face of any patent issuing from the child application.

More information is available on the Access to Relevant Prior Art webpage of the USPTO website.

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The demand for improvements in autonomous technology is accelerating. Memory and processing power has continued to grow exponentially cheaper, but the volume of data to process has exploded making it nearly impossible for traditional data analysis techniques to provide timely and cost effective guidance. Whether the data arrives in real time, or is acquired and stored for later analysis, the need for systems that can generate valuable insights from mountains of raw information will only increase. Consequently, neural network technology is increasingly valuable to organizations large and small making it a prime target for intellectual property protection. However, a number of misconceptions have arisen regarding the patentability of neural network technology.

Software isn’t patentable, so neural nets aren’t either: Software inventions are patentable, but the Patent Office and the Courts have narrowed the scope of what is patentable by requiring that the patent claims must be directed to something more than a well-known or abstract concept implemented on a computer. This is especially interesting where neural networks are involved because in some cases, the network itself is not new. The network topology (i.e. number of nodes, number of layers, the connections between them, etc.) may not be new, and perhaps the activation functions or backpropagation techniques used by the network are also not new. The training data sets or data preprocessing techniques may not be new either, but the outcome of using such a neural network may be truly revolutionary. Thus it is important to plan ahead in the drafting process to include aspects like tangible data sources and physical sensor input/output, control of physical objects or machines, and information about what technical problems are being overcome and how.

I didn’t invent a neural network so this is probably not patentable: Keeping the right focus on the invention is a fundamental issue that sometimes hampers patentability for software inventions, and it can be particularly problematic where neural networks are involved. Is the “magic” in the neural network or is the magic in how that network is used? For example, is the concept a new topology for a neural net that is more efficient, yields better results, or solves a particular problem? Is it a new activation function, or a new type of backpropagation scheme? Is the invention a new gradient descent algorithm that is optimized for a particular problem space? If any of these are the case, then the claims and disclosure should focus more on the network itself and how it is configured. On the other hand, is the invention a system that works better because it uses a neural network? If the invention is an improvement on neural networks, then more details about the network itself will be needed to show the technical problem and solution. If the invention is an improvement in some other field of endeavor that happens to involve a neural network, then more information about the inputs, outputs, and operation of the device will be needed, and perhaps less information about the neural network itself. Determining what the invention is will drive what kind of disclosure is needed in order to obtain a patent.

I’ll file the application but keep the real invention secret: The patent system grants the right to stop others from making, using, or selling patented inventions. In return, inventors are required to teach the world how to make the invention. In the case of neural networks, this can be tricky because many of the details about how a neural net reaches a given result are unknown until it is put to use, or they may be different from one execution to the next, or in some cases they are simply unknowable without extreme effort. In some cases, billions of permutations of inputs, outputs, and the corresponding weights for each node in the network could exist, but only after the network is put to use. That said, trying to patent a concept while keeping it secret is not permitted. The invention must be disclosed in such a way that a person of ordinary skill in the field could make and use the invention. The claims may be allowed to describe inputs and outputs at a high level, but at least some explanation is required as to how the system uses them and how they interact with other components of the system. Usually, more disclosure is better than less because failure to adequately explain the workings of a neural network, or the use of it, may cost both the opportunity to patent the concept, and the opportunity to protect the invention some other way, such as by trying to keep it a trade secret. With a little careful planning, both of these negative outcomes can usually be avoided.

The Indianapolis Star published an interesting article about the history of Gatorade® thirst quencher: The Fascinating Tale of Gatorade’s Indy Beginning

Our law firm has played a key role in this famous product over the decades. We prepared and secured the original patent rights for the product. We also secured the original trademark registrations for “GATORADE” mark. We continue to represent the University of Florida, home of the Gators, in connection with the Gatorade® brand.

Woodard, Emhardt, Henry, Reeves & Wagner, LLP partner, Lisa A. Hiday, celebrates 25 years of service with the firm. Over the years, Ms. Hiday became an integral member of the litigation team, litigating patent, trademark and copyright disputes across a wide range of industries, including industrial manufacturing, medical devices, pharmaceutical technologies, packaging, and computerized systems services.

Please join us in wishing Ms. Hiday a happy anniversary, congratulating her on this milestone and thanking her for 25 years of service to clients and the Firm.