Posts Tagged: "machine learning"

In general, the adoption of artificial intelligence (AI) and machine learning technologies has the potential to impact society in many ways. These technologies can automate tasks and make them more efficient, which can lead to job displacement and other economic impacts. They can also be used to make decisions that affect people’s lives, such as in the criminal justice system or in hiring, which raises ethical concerns. Additionally, the development and use of AI and machine learning technologies can raise issues related to privacy and security. What could be a more fitting way to open a 2022 year-in-review article on AI and machine learning than by asking OpenAI’s newly beta-released ChatGPT tool to contribute? The above paragraph was generated using ChatGPT’s conversational, chat-based dialog input. The initial request of ChatGPT was the prompt: “Explain the social impacts of artificial intelligence and machine learning technologies over the past year.”

In Part I of this series, we discussed the Federal Trade Commission’s (FTC’s) case against Everalbum as just one example where companies may be required to remove data from their machine learning models (or shut down if unable to do so). Following are some additional pitfalls to note. A. Evolving privacy and data usage restrictions Legislators at the international, federal,…

A widespread concern with many machine learning models is the inability to remove the traces of training data that are legally tainted. That is, after training a machine learning model, it may be determined that some of the underlying data that was used to develop the model may have been wrongfully obtained or processed. The ingested data may include files that an employee took from a former company, thus tainted with misappropriated trade secrets. Or the data may have been lawfully obtained, but without the adequate permissions to process the data. With the constantly and rapidly evolving landscape of data usage restrictions at the international, federal, state, and even municipal levels, companies having troves of lawfully-obtained data may find that the usage of that data in their machine learning models becomes illegal.

Patent categories were established more than 100 years ago. There are dozens of categories that reflect industry at the time: gears, sewing machines, and bicycles, to name a few. While these are certainly useful categories, the patent classification system has not kept up with the times. It leaves out many modern technologies, like inventions that are based on machine learning or blockchain. There are no categories for these innovations, which are reshaping our world in real-time. The problem? When patent classifications don’t actually classify inventions, we have no way of knowing how many inventions in these categories are being registered.

The managers of most companies tend to see information security as a Lord of the Rings problem, with the focus on protecting the perimeter. This reflects the popular view. Indeed, from reading headlines about hackers, you might think that cybercrime –malign attacks from evil outsiders – represents the most common way that commercial information is lost. And you would be wrong. It’s not the overlooked vulnerability in the company’s firewall that gets exploited by determined external enemies. Instead, it’s the careless employee who overshares on social media, brags at parties, or leaves a sensitive document in an airport lounge. (Remember traveling on planes?)

Since the Alice decision, the U.S. courts have adopted different views related to the role of the preemption test in eligibility analysis. While some courts have ruled that lack of preemption of abstract ideas does not make an invention patent-eligible [Ariosa Diagnostics Inc. v. Sequenom Inc.], others have not referred to it at all in their patent eligibility analysis. [Enfish LLC v. Microsoft Corp., 822 F.3d 1327] Contrary to those examples, recent cases from Federal Courts have used the preemption test as the primary guidance to decide patent eligibility. Inventive concepts enabled by new algorithms can be vital to the effective functioning of machine learning systems—enabling new capabilities, making systems faster or more energy efficient are examples of this. These inventions are likely to be the subject of patent applications. However, the preemption test adopted by U.S. courts may lead to certain types of machine learning algorithms being held ineligible subject matter.

Much has been written about how artificial intelligence (AI) and machine learning (ML) are about to transform the global productivity, working patterns and lifestyles and create enormous wealth. Gartner projects that by 2021, AI augmentation will create $2.9 trillion of business value and $6.2 billion hours of worker productivity globally. McKinsey forecasts AI potentially could deliver additional economic output of around $13 trillion by 2030, boosting global GDP by about 1.2 percent a year. Companies around the globe are all racing to adopt and innovate AI and ML technologies. Indeed, by any account, much progress has been made and the adoption and innovation rates are quickening. But who is winning or leading in the race? A quick review of U.S. patent data may provide a glimpse into the state of the race.

When discussing patentable inventions with data scientists, I often hear them dismiss their inventions under arguments such as these: “We’re using the same tools as everyone else,” “Augmenting data for the training set is well known,” “A similar thing has been done for car-bumper design” (said by the designer of a churro-making machine), “Configuring the neural-network hyperparameters is trivial,” and worst of all, “It’s obvious.” Data scientists often believe that their accomplishments are not patentable, but in-depth exploration of their work often uncovers patentable ideas. I am referring to data scientists that use machine-learning (ML) tools to uncover intrinsic relationships within a large corpus of data. Other data scientists design and improve these ML tools, and their work may also result in patentable ideas, which is a topic for discussing another day.

I recently had the opportunity to speak on the record with three examiners at the European Patent Office (EPO) about their advice, pet peeves, and approaches to examining computer implemented inventions, particularly in the field of artificial intelligence (AI), and how the EPO compares with the U.S. patent examination system. It was a wide-ranging and thoroughly enjoyable conversation with three professionals who obviously know this area very well, and who were willing to provide keen insight into ways applicants can and should improve technical disclosures to maximize the likelihood of obtaining a patent.