Machine Learning at the Patent Office: Lessons for Patents and Administrative Law

By Rai, Arti K. | Iowa Law Review, July 2019 | Go to article overview

Machine Learning at the Patent Office: Lessons for Patents and Administrative Law


Rai, Arti K., Iowa Law Review


I. Introduction

A voluminous body of scholarly and popular commentary discusses the use of predictive algorithms by government actors. The decision rule in question can be explicitly specified by humans. Conventional linear regression, for example, is a specific, human-generated data model that transforms inputs into outputs.1 Alternatively, the decision rule can emerge from algorithmic or machine learning. Although machine learning encompasses many algorithms of varying complexity, a distinctive feature of the genre is that the learning algorithm does not represent the decision rule; instead, the algorithm "learns" the decision rules from data known as training data.2

In both cases, the commentary has often been highly critical, particularly in addressing deployment of algorithms in areas like predictive policing and criminal risk assessment.3 Commentators have expressed concern about classification based on legally protected characteristics and inaccurate adjudication of individual rights.4

understandably, legal commentators have paid less attention to decisionmaking contexts where bias and rights are not first-order concerns.5 Yet those contexts, which can involve decisions that are quite important for social welfare, are also worthy of study.

Within the spectrum of agency action, the patent examination practices of the U.S. Patent and Trademark Office ("Patent Office" or "USPTO")6 represent one such case.7 The Patent Office receives hundreds of thousands of patent applications every year, and the examiners who process the applications operate under severe time pressure. Scholars differ over whether granted patents should be viewed strictly through a consequentialist lens.8 But most analysts would agree that examination of patent applications has a strong consequentialist flavor. Relatedly, because patent applicants do not have property rights in applications,9 constitutional due process limitations on examination may be less constraining, both doctrinally and normatively, than limitations on the cancellation of granted patents.

Perhaps not surprisingly, then, a number of commentators who have discussed the strenuous workload burden that patent examiners face have noted in passing the applicability of machine learning.10 As they have mentioned, machine learning could be particularly useful for the timeintensive but critical task of searching the prior learning ("prior art") to determine whether, at the time of patent filing, the invention claimed was novel and nonobvious. Indeed, even though patent law does not require patent applicants to do a prior art search, the patent services marketplace now includes firms that purport to perform such searches using machine learning.11

As it happens, the Patent Office has begun efforts to use machine learning in the area of prior art search.12 Thus far, these efforts have largely gone unexamined in the legal literature, particularly from the perspective of administrative law and policy. The Patent Office's foray into machine learning not only provides a window into potential improvement of the patent system, but it also offers lessons that may generalize to other agencies whose processing of large volumes of information does not implicate bias or individual rights.

The most important generalizable challenge involves the complex normative goal of explainability.13 Thus far, the Patent Office has appeared to stress transparency to the general public as necessary for achieving explainability.14 The relationship between explainability and transparency must, however, be parsed carefully, in a manner that is attentive to context. In contexts like prior art search, such parsing reveals that full transparency is not necessary for achieving an adequate level of explainability.

That said, it is important to recognize that the Patent Office is in a difficult position. Stakeholders that seek to secure patents as well as the Patent Office's reviewing court, the Court of Appeals for the Federal Circuit, heavily scrutinize the Patent Office's decisions. …

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