Artificial Intelligence Patent Applications
Whether you are developing an AI/ML-based invention or trying to obtain patent protection for an existing application, there are a number of important considerations you should keep in mind. These include separating the training phase from the execution phase, providing a patentable hook, and identifying the elements of the structure that are patentable. For a machine learning model to be patented, it must meet the technical character test. This requires a substantive correlation between the abstract conceptual elements and the technical elements. For example, an unsupervised learning model may require a processing unit to perform complex calculations. It also must be deployed in a commercial setting. The United States Supreme Court has determined that AI-based inventions that employ computer processes are eligible for patent protection. However, this determination is only applicable to those inventions that meet certain requirements. For instance, a neural network classifier system uses physical data to train a classifier. It can be trained using a training dataset or through back propagation. The training process is typically carried out by a computer program. Although the patent specification is generally broken into major components of the model, you may need to break it down further. This can help to prevent split infringement issues. In addition to describing the method or procedure in detail, you should also provide a detailed description of the main embodiment. This may include a physical device such as a computer, or a special hardware used in the training and execution phases. In addition, you may want to incorporate as much physical structure as possible into your invention. This can obviate some of the issues associated with 35 U.S.C. Section 101.Ways to avoid Alice rejection during examination
The Alice (Abstract Idea) test is used by the U.S. Patent and Trademark Office (USPTO) to determine whether a claimed invention is eligible for a patent. The test was established in 2014 by the U.S. Supreme Court in the Alice v. CLS Bank case, and it requires the USPTO to determine whether the claimed invention is directed to a “patent-ineligible abstract idea” or not.
Here are some best practices for avoiding an Alice rejection in patent drafting:
- Identify the Invention: Clearly define the invention and distinguish it from any abstract ideas or concepts.
- Provide Specific Details: Provide specific details about the invention, including how it works and how it is different from prior art.
- Avoid Overly Broad Claims: Avoid overly broad claims that could be seen as directed to an abstract idea. Instead, focus on specific aspects of the invention that are new and innovative.
- Emphasize Technological Advancement: Emphasize the technological advancement of the invention and how it solves a specific problem or provides a new benefit.
- Provide Examples and Use Cases: Provide examples and use cases that demonstrate the practical application of the invention and how it solves a specific problem.
- Use Claims That Are Directed To A Specific Implementation: Use claims that are directed to a specific implementation of the invention rather than abstract concepts.
- Provide Examples on How Your Claimed Invention Improves Machine Performance: Read the USPTO Guidance on navigating the Alice test and can help ensure that your claims are properly drafted.
Examples of improving processor performance
Section 101 of the U.S. Patent Act sets forth the criteria for determining whether a claimed invention is eligible for a patent. In terms of improving processor performance, here are some examples of inventions that could potentially pass the Section 101 eligibility test:
- Cache memory management techniques that improve the efficiency of data retrieval and processing.
- Improved algorithms for task scheduling and resource allocation that increase processor utilization.
- New techniques for pipelining and parallel processing that speed up data processing.
- Innovations in processor architecture, such as the use of multiple cores or specialized processing units, that improve performance.
- Improved techniques for data compression and encoding that reduce the amount of data that must be processed, resulting in faster processing times.
- Improved error correction techniques that reduce the number of processing errors and improve overall processor performance.