Intellectual Property & Technology Law

Singapore puts intellectual property focus on innovation, intangible assets


"Technological and market changes in the digital economy significantly affect how data is created, distributed, and consumed," the Singapore IP Strategy 2030 report stated. "The'big data revolution' has seen huge growth in the generation and collection of digital data, and driven the creation of large datasets and databases that are the'stock feed' on which new technologies such as machine learning rely on." It added that IPOS would continue to assess the importance of big data in Singapore and determine if further changes to its IA and IP laws were needed to enable innovators and enterprises to "capture economic opportunities" in the digital economy. The aim here was to find "a balance" between the interest of creators and providing access to third parties. IPOS also was in the midst of reviewing the country's regime for trade secrets protection to ensure it remained conducive for innovative businesses. In addition, with AI increasingly used in create products, inventions and content, the IP office was assessing Singapore IP regime so it supported the development and use of AI technologies.

Machine Learning, Ethics, and Open Source Licensing (Part I/II)


The unprecedented interest, investment, and deployment of machine learning across many aspects of our lives in the past decade has come with a cost. Although there has been some movement towards moderating machine learning where it has been genuinely harmful, it's becoming increasingly clear that existing approaches suffer significant shortcomings. Nevertheless, there still exist new directions that hold potential for meaningfully addressing the harms of machine learning. In particular, new approaches to licensing the code and models that underlie these systems have the potential to create a meaningful impact on how they affect our world. This is Part I of a two-part essay.

Artificial Intelligence Update


These advances will create a network where almost every device can be simultaneously connected, enabling technologies not possible today. Governments and private entities are just beginning to invest in the technology, and projections suggest commercial availability around 2030. But given 6G's anticipated ubiquity and potential to change the landscape, we would be wise to begin learning about it now. Artificial intelligence ("AI") represents a new frontier in the global economy: Some estimates say it could contribute up to $15.7 trillion worldwide by 2030. Increases in computing power and innovations in computer science have fueled AI innovation.

Evolution of IP protection for artificial intelligence in France


Artificial intelligence (AI) is set to transform many aspects of our lives, including our home and health. AI is already widely used in internet searches, and home devices with speech recognition, but in the near future we will see AI become even more widespread. This will have significant repercussions as AI performs many tasks that until now could only be undertaken by humans. AI will remove human intervention from much of the picture. This will particularly affect intellectual property law.

7 lessons to ensure successful machine learning projects


When Michelle K. Lee, '88, SM '89, was sworn in as the director of the U.S. Patent and Trademark Agency in 2015, she saw an opportunity. The agency was a bit behind on digital transformation and adopting things like cloud computing and artificial intelligence, but the organization had mountains of data -- like more than 10 million patents the office has issued since opening in 1802, and 600,000 patent applications received each year. Lee led a project to use data and analytics to modernize the agency, such as implementing AI solutions to improve patent searches and the speed and quality of patents issued. By gathering data about how patent examiners make decisions, and determining outlying behavior, the office could also pinpoint areas in which examiners would benefit from targeted training. "If the U.S. Patent and Trademark Office, a 200-plus-year-old governmental agency, has a machine learning opportunity, so too does every organization," Lee said during a presentation at EmTech Digital, hosted by MIT Technology Review.

Is IP Law Ready for AI?


Speaking to established patent attorney Nick Transier, we explore why there has been a boom in AI and the special considerations behind AI patents.

Arm v9 promises ray tracing for smartphones and a big performance boost


Arm said Tuesday that ray tracing and variable rate shading will migrate from the PC to Arm-powered smartphones and tablets as part of Armv9, the next-generation CPU architecture that the company expects will power the next decade of Arm devices. Chips based upon the v9 architecture will be released in 2021, providing an estimated 30-percent improvement in performance over the next two Arm chip generations and the devices that run them. Arm's v9 will also add SVE2, new AI-specific instructions that will probably be used for the AI image processing used on smartphones, such as portrait mode. Arm v9 will also include what Arm is calling Realms, a hardware container of sorts specifically designed to protect virtual machines and secure applications. As an intellectual-property licensing company, Arm enjoys a unique position in the computing industry.

Are AI-generated inventions patentable?


What does a chair from furniture manufacturer Kartell have in common with a rocket engine by the software powerhouse Hyperganic? They were both created by generative design -- in other words, made by AI. But it's a far cry from simple CAD design, using algorithms created by AI to generate a first set of designs for a product based on certain input parameters. It will then continue to refine these designs with each iteration until the final product materialises. Combined with industrial 3D printing, the result is a technically superior product that weighs less, has better functional features and is often less prone to wear and tear.

Responsible machine learning can still protect intellectual property. Here's how


Two key components for using ML responsibly provide a prudent "start here" for organizations: model explainability and data transparency. The inability to explain why a model arrived at a particular result presents a level of risk in nearly every industry. In some areas, like healthcare, the stakes are particularly high when a model could be presenting a recommendation for patient care. In financial services, regulators may need to know why a lender is making a loan. Data transparency can ensure there is no unfair or unintended bias in the training data sets used to build the model, which can lead to disparate impact for protected classes – and consumers have what is increasingly a legally protected right to know how their data is being used.