ICLE Director of Law & Economics Programs Gus Hurwitz joined Steptoe & Johnson’s The Cyberlaw Podcast to discuss Elon Musk’s bid to buy Twitter and how it has revealed hypocrisies on both the right and left.

Abstract

In this Article, we describe how to use artificial intelligence (AI) techniques to partially automate a type of legal analysis, determining whether a patent claim satisfies the definiteness requirement. Although fully automating such a high-level cognitive task is well beyond state-of-the-art AI, we show that AI can nevertheless assist the decision maker in making this determination. Specifically, the use of custom AI technology can aid the decision maker by (1) mining patent text to rapidly bring relevant information to the decision maker attention, and (2) suggesting simple inferences that can be drawn from that information.

We begin by summarizing the law related to patent claim indefiniteness. A summary of existing case law allows us to identify the types of information that can be relevant to the legal determination of indefiniteness. This in turn guides us in designing AI software that processes a patent text to extract information that can be relevant to the legal analysis of indefiniteness. Some types of relevant information include whether terms in a claim are defined in the patent, whether terms in a claim are not mentioned in the patent specification, whether the claim includes nonstandard terms coined by the drafter of the patent, whether the claim relies on vaguely-specified measurements, and whether the patent specification discloses structure corresponding to a means-plus-function limitation.

The AI software rapidly processes a patent text and identifies information that is relevant to the legal analysis. The software then provides the human decision maker with this information as well as simple metrics and inferences, such as the percentage of claim terms that are defined explicitly or by example, and whether terms that are coined by the drafter should be defined or renamed. This can provide the user with insights about a patent much faster than if the user read the entirety of the patent to locate the same information unaided.

Moreover, the software can aggregate the various types of information to “score” a claim (e.g., from 0 to 100) based on its risk of being deemed indefinite. For example, a claim containing only defined terms and lacking any vague measurements would score much lower in terms of risk than a claim with terms that are not only undefined but do not even appear in the patent specification. Once each claim in a patent is assigned such an indefiniteness score, the patent itself can be given an overall indefiniteness score.

Scoring groups of patents in this manner has further advantages even if the scores are blunt measurements. AI software ranks a group of patents (e.g., all patents owned by a company) by indefiniteness scores. This allows a very large set of patents to be quickly searched for patents that have the highest, or lowest, indefiniteness score. The results of such a search could be, e.g., the patents to target for detailed review in litigation, post-grant proceedings, or licensing negotiations.

Finally, we present some considerations for refining and augmenting the proposed methods for partially automating the indefiniteness analysis, and more broadly other types of legal analysis.

The States brought an antitrust complaint against Facebook alleging that various conduct violated Section 2 of the Sherman Act. The ICLE brief addresses the States’ allegations that Facebook refused to provide access to an input, a set of application-programming interfaces that developers use in order to access Facebook’s network of social-media users (Facebook’s Platform), in order to prevent those third parties from using that access to export Facebook data to competitors or to compete directly with Facebook.

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The Federal Trade Commission (FTC) is at it again, threatening new sorts of regulatory interventions in the legitimate welfare-enhancing activities of businesses—this time in the realm of data collection by firms.

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Teaching the economics of innovation to college students isn’t a tough job. They understand intuitively that pro-competitive policy maximizes their ability to choose the best product for them. More options for consumers leads to opportunities to optimize their choices. If the cost of one option is too high, for whatever reason, consumers can choose something else.

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A raft of progressive scholars in recent years have argued that antitrust law remains blind to the emergence of so-called “attention markets,” in which firms compete by converting user attention into advertising revenue. This blindness, the scholars argue, has caused antitrust enforcers to clear harmful mergers in these industries.

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The antitrust debate on app store practices is all over the news almost every day. Last month, the Paris Commercial Court fined Google for abusive dealings with developers, while the Dutch competition authority issued a tenth weekly penalty payment against Apple for failing to comply with its decision to allow dating apps on its App Store to use non-Apple methods of payment. Yet, the US Court of Appeals for the Ninth Circuit will soon rule in Epic’s appeal over the Californian District Court ruling in its case against Apple.

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Abstract

The institution of bankruptcy law seeks to facilitate economic efficiency by enabling the reorganization of economically viable but financially distressed firms and facilitating the liquidation of economically failed firms. Does the U.S. Chapter 11 bankruptcy process perform this filtering function efficiently? Using data from large public bankruptcies between 1981-2010, we find that it does not. Specifically, (1) evidence on matched performance differences between bankrupt firms and industry counterparts indicate that there is no improvement in the performance gap between bankrupt firms and industry right before and after bankruptcy, and, (2) firms emerging from bankruptcy do not exhibit financial performance catch-up behavior to their going concern industry counterparts. In addition, we find (3) judicial bias in favor of reorganization in cases involving firms with more employees and operations closer to the judge’s district, suggesting that bankruptcy judges respond to social-political considerations, when deciding whether to reorganize the firm.

Federal Trade Commission (FTC) competition rulemakings, like spring, are in the air. But do they make policy or legal sense?

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