Today, companies are developing artificial intelligence (AI) systems to meaningfully analyze the deluge of biomedical data. A substantial investment in building and deploying machine learning (ML) technology—the most active area of AI technology being developed today—warrants carefully considering how to protect the resulting intellectual property (IP), but there are challenges to doing so. In this article, we explore strategies of protecting IP for ML technology, including what aspects to consider patenting given current and ongoing changes to U.S. patent law, and when to consider trade secret protection.

What Is Machine Learning?

Generally, developing an ML system involves creating and deploying a computer program having a model whose performance on some task improves as additional data is used to train the model. In the life sciences, such data can include medical images, genomic data, and electronic health records.

For example, an ML model may be trained on magnetic resonance (MR) images to recognize whether a previously unseen MR image of a patient's brain shows a hemorrhage. As another example, an ML model may be trained on genomic data for individuals with a particular cancer to predict whether a patient's genome has features indicative of the cancer.

Today, neural networks are a popular class of ML models widely used, and are often referred to as "deep learning" in a nod to their multi-layer (deep) structure. Other ML models include Bayesian models, decision trees, random forests, and graphical models. Indeed, rapid development of various ML tools has led to an explosion of activity in applying them to new problems across diverse fields.

Machine Learning IP—Patent Protection

Deploying an ML system typically involves: (1) selecting/ designing an ML model, (2) training the ML model using data, and (3) deploying and using the trained ML model in an application. Valuable IP may be generated at each of these stages, and it's worth considering protecting it through patents. There, however, are a number of challenges in patenting ML systems.

Novelty and Obviousness

An invention must be new and non-obvious to be patented. This makes it difficult to patent the use of off-the-shelf ML technology even if in the context of a new application. Simply downloading freely available ML software, providing it with data, and displaying the results (e.g., to a doctor or researcher) may be viewed by the U.S. Patent and Trademark Office (USPTO) as failing to clear the non-obviousness hurdle. After all, the freely available ML software is distributed precisely so that people can perform this exact process—why, then, would it not be obvious to do so?

But in reality, building and deploying ML systems requires more work beyond simply downloading and running software. Focusing patent claims on the results of such efforts will lead to greater success. Here are three examples of potentially patentable aspects of an ML system:

  1. New ML model. In deploying ML technology, a new model may have been developed (e.g., new neural network architecture). Claiming novel aspects of the model will help to address novelty and nonobviousness challenges.
  2. Training an ML model. Innovative ways of generating training data and/or a new training algorithm may be claimed. For example, when there is insufficient training data, it may be augmented by synthesizing new training data from old training data or other sources, and such data augmentation techniques may be innovative and the focus of patent claims.
  3. Deploying an ML model. How an ML model is integrated into an application may provide a novelty and nonobviousness hook. Claims focusing on integration and deployment should go beyond merely displaying the model's output and focus on what the output is used to achieve. For example, applying an ML system to medical images may result in instructions to take more images with different settings because the ones obtained are unsatisfactory. Other examples include choosing among different next steps in a control system, customizing a patient's treatment, or updating a clinical trial. When an ML system is deployed in conjunction with a specialized device (e.g., an imaging device, a sequencing device), rather than merely a computer, claims could focus on how the ML system is integrated with the device.

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