Top Tens in 2016: Patent, Trademark, Copyright and Trade Secret Cases

The Supreme Court issued several rulings that affect incentives in patent law. The Court relaxed the standard for the award of treble damages, narrowed the damages awards for infringement of design patents, and upheld key parts of the new procedures for challenging the validity of patents before the United States Patent and Trademark Office. After numerous decisions holding claimed inventions to be outside patentable subject matter in the wake of the Supreme Court’s 2014 Alice decision, the Federal Circuit rejected some challenges on those grounds, evincing a split among the circuit’s judges on the bounds of patentable subject matter. Several decisions affected legal boundaries: whether federal copyright law preempted state law rights for resale royalties and right of publicity, and whether trade secret law preempted claims for unjust enrichment. Courts continue the trend to limit injunctions, where parties have delayed or seek overbroad orders. Trademark decisions stretched the limits of the Lanham Act, to reach foreign sales of goods bought in the US and protection of foreign marks within the US, as well as triggering nationwide US trademark protection upon the most minor use of a mark. In copyright, evergreen issues include the scope of fair use and the conditions for immunity for internet service providers. Trade secret saw a new federal trade secret act, along with cases on all the major elements of trade secret misappropriation, in such areas as beehive foraging and bacon cooking. This paper also uses the collection of cases to conjecture a little on cognitive factors that affect judicial reasoning. Judges have the same cognitive biases as other humans. We can read judicial decisions, speculatively, with an eye toward how the judges unconsciously seek cognitive ease. The paper also discusses how the holdings of some cases may affect subsequent the decision process in some intellectual property matters

The GPL Meets the UCC: Does Free Software Come with a Warranty of No Infringement of Patents and Copyrights?

The GNU General Public License, known as the GPL, is the cornerstone of free software. The GPL has served as the organizing document for free software, providing a structure that has helped transformed the development of software and electronic devices. Software licensed under the GPL may be freely copied and adapted. The source code for the software is made available, to enable anyone to study and change it. The GPL does have copyleft restrictions, intended to keep the software free for others. If someone adapts and redistributes GPL’d software, they must likewise allow access to their source code. The GPL states that the code is provided \u27AS IS\u27 WITHOUT WARRANTY OF ANY KIND. The clause may not be quite accurate. The licensor makes no warranty of quality that the software will work. But, due to idiosyncrasies of the Uniform Commercial Code, someone who sells software under the GPL may – unknowingly – make a warranty of noninfringement, promising that use of the software does not infringe any patents, copyrights or other third party rights. Someone who sells software under the GPL might be liable for damages, if the buyer were sued by a third party claiming patent or copyright infringement. This paper works through the relevant legal code, assesses the risk to developers, sellers of devices with embedded software and other licensors, and suggests practical ways to reduce the risk

Top Tens in 2010: Patent, Trademark, Copyright and Trade Secret Cases

This piece discusses notable intellectual property decisions in 2010 in the United States. Viewed across doctrinal lines, some interesting threads emerge. The scope of protection was at issue in each area, such as whether human genes and business methods are patentable, whether a product idea may be a trade secret, and where the constitutional limits on copyright legislation lie. Secondary liability remains widely litigated, as rights holders seek both deep pocket defendants and a means to cut off individual infringers. The courts applied slightly different standards as to the state of mind required for secondary liability. Many of the cases involved disputes between hiring and hired parties, over the ownership of intellectual property rights:professors and universities contesting rights to federally funded inventions; an artist seeking to prevent a museum from showing an unfinished commissioned work; a party that commissioned a sculpture, but without obtaining the copyright, relying on fair use to exploit derivative works; entrepreneurs disputing how to apply the work-made-for-hire doctrine in the informal context of a start-up business; and a company hiring a competitor’s employees to reverse engineer its trade secrets. A number of cases concerned the relationship between intangible rights and physical property: liability for false patent marking, attempts to limit a biotech patent to the sample submitted to show possession of the invention, seeking trademark protection for the shape of a round beach towel, and sales of second hand software on eBay

Assignability of Letter of Credit Proceeds: Adapting the Code to New Commercial Practices

The right to receive payment under a letter of credit may be assigned, even if the letter of credit prohibits assignment of proceeds. This article argues that this rule should be changed, to give effect to clauses barring assignment of proceeds. The rule made sense where letters of credit were primarily used in sales of goods transactions. In that context, the rule simply mirrored the contract law doctrine that the right to receive payment under a sales contract may be freely assigned (subject to any defenses the payor might have). But letters of credit are not used in a broader range of transactions, and are often used as a standby security device, as opposed to a primary means of payment. Where such letters of credit are more like collateral than a means of payment, the parties should be free to agree on the structure of the transaction, including limits on assignment. In addition, the parties can effectively bar assignment by drafting the conditions for drawing the letter of credit. The bar against assignment then, is both outdated and ineffective (and thereby inefficient, because the parties must expend some resources in structuring and drafting around the rule)

Fair Use and Machine Learning

There would be a beaten path to the maker of software that could reliably state whether a use of a copyrighted work was protected as fair use. But applying machine learning to fair use faces considerable hurdles. Fair use has generated hundreds of reported cases, but machine learning works best with examples in greater numbers. More examples may be available, from mining the decision making of web sites, from having humans judge fair use examples just as they label images to teach self-driving cars, and using machine learning itself to generate examples. Beyond the number of examples, the form of the data is more abstract than the concrete examples on which machine learning has succeeded, such as computer vision, viewing recommendations, and even in comparison to machine translation, where the operative unit was the sentence, not a concept that could be distributed across a document. But techniques presently in use do find patterns in data to build more abstract features, and then use the same process to build more abstract features. It may be that such automated processes can provide the conceptual blocks necessary. In addition, tools drawn from knowledge engineering (ironically, the branch of artificial intelligence that of late has been eclipsed by machine learning) may extract concepts from such data as judicial opinions. Such tools would include new methods of knowledge representation and automated tagging. If the data questions are overcome, machine learning provides intriguing possibilities, but also faces challenges from the nature of fair use law. Artificial neural networks have shown formidable performance in classification. Classifying fair use examples raises a number of questions. Fair use law is often considered contradictory, vague, and unpredictable. In computer science terminology, the data is “noisy.” That inconsistency could flummox artificial neural networks, or the networks could disclose consistencies that have eluded commentators. Other algorithms such as nearest neighbor and support vectors could likewise both use and test legal reasoning by analogy. Another approach to machine learning, decision trees, may be simpler than other approaches in some respects, but could work on smaller data sets (addressing one of the data issues above) and provide something that machine learning often lacks: transparency. Decision trees disclose their decision-making process, whereas neural networks, especially deep learning, are opaque black boxes. Finally, unsupervised machine learning could be used to explore fair use case law for patterns, whether they be consistent structures in its jurisprudence, or biases that have played an undisclosed role. Any possible patterns found, however, should be treated as possibilities, pending testing by other means

Top Tens in 2015: Patent, Trademark, Copyright and Trade Secret Cases

The Supreme Court significantly affected the dynamics of patent litigation, holding that patent claim interpretation was not always reviewed de novo and that good faith belief that a patent was invalid was not a defense to infringement. The Federal Circuit potentially changed the approach to patent claim interpretation, holding that claims could be interpreted in light of the written description of the invention, even where the claim was not ambiguous. The Federal Circuit also addressed inducement of patent infringement, holding that it was not inducement to suggest consulting a physician who would likely prescribe an infringing treatment. The Federal Circuit also held that two parties acting in concert could infringe a patent, replacing its rejected doctrine of divided infringement. Trademark saw rejection of trade dress protection for cell phone design and conflicting opinions on whether disparaging trademarks are registrable. Copyright cases show that fair use authorized the Google Book project and also protected against attempts to use copyright to censor critics. Courts addressed some classics of copyright courses, including the copyrightability of maps, recipes, and “Happy Birthday to You.” Trade secret cases emphasized the fundamental requirements, rejecting attempts to give trade secret protection where parties had failed to take the necessary reasonable security measures

Top Tens of 2013: Patent, Trademark, Copyright, and Trade Secret Cases

Top Tens of 2013: Patent, Trademark, Copyright, and Trade Secret Case