AI Inventions are Patentable
The United States has long recognized the patentability of inventions embodying Artificial Intelligence (AI). Some of the earliest AI patents include U.S. Patent No. 3,308,441 (filed in 1963), relating to an artificial neural network, and U.S. Patent No. 3,287,649 (also filed in 1963), relating to an audio signal pattern recognition using a neural network.
The U.S. Patent and Trademark Office (USPTO) grants patents for AI-based inventions as long as they meet the requisite criteria of novelty, non-obviousness, utility, and patent-eligibility (described below). Whether it is a novel machine learning algorithm, a unique AI hardware component, or a groundbreaking application of neural networks, the U.S. patent system provides a conduit for inventors to secure the exclusive rights to their AI-driven inventions.
While AI can be a powerful tool in the development of inventions, U.S. law currently states that only natural persons can be named as inventors on U.S. patents. Mr. Stephen Thaler tested this policy by filing for patent protection on an invention that was invented solely by an artificial intelligence system. The USPTO rejected the application, insisting that only human inventors can be granted patent protection. The Federal Circuit upheld this decision (see Thaler v. Vidal).
Technologies Where AI Patents Have Been Granted
Artificial Intelligence (AI) has ushered in a new era of innovation, and the United States Patent and Trademark Office (USPTO) has been bustling with patents related to various AI technologies. Among these, Generative AI stands out for its prowess in creating new data or content that bears a statistical resemblance to a given dataset. This technology, leveraging algorithms like Generative Adversarial Networks, finds applications in diverse fields such as creating realistic images, video generation, and text-to-speech synthesis, opening avenues for patents that encompass the generation of novel, high-fidelity data.
Another noteworthy AI type is Reinforcement Learning (RL), which is centered around an agent learning to make decisions by interacting with an environment. Through a system of rewards and penalties, the agent learns optimal strategies for decision-making, a principle that has been employed in various fields including robotics, game playing, and autonomous systems. Patent activity in RL revolves around novel algorithms and systems that enable machines to learn complex behaviors and make intelligent decisions over time.
Moreover, Neural Symbolic Computing represents a fusion of neural networks with symbolic reasoning, aiming to harness the strengths of both machine learning and logic-based AI. This hybrid approach enables the learning of structured representations from data and the reasoning over these representations in a symbolic manner. Patents in this realm may cover novel architectures, learning algorithms, and applications that meld symbolic reasoning with sub-symbolic learning.
These emerging AI technologies join the ranks of established fields like Knowledge Processing, Speech Recognition, and AI Hardware, among others. For instance, Knowledge Processing encapsulates methods to represent facts about the world and derive new knowledge from a database, forming the foundation of expert systems. Similarly, AI Hardware, exemplified by innovations like Google's Tensor Processing Unit (TPU), encompasses the physical components necessary to implement AI software, marking significant strides in enhancing the efficiency of running neural network algorithms. The vibrant and diverse nature of AI technologies, each with its unique methodologies and applications, reflects a rich and growing landscape of AI-related patents, showcasing the boundless potential of AI to drive innovation across a myriad of sectors.
Section 101 Issues
As is true for other software inventions, patent applications covering artificial intelligence must be considered eligible for patent protection under Section 101 of the Patent Act before a patent will be granted. In particular, the application must be considered "patent eligible" under the guidelines created by the Supreme Court in its Alice Corporation decision (2014). Whether or not a particular AI invention is eligible for patent protection is, unfortunately, a complicated issue. While the statutory basis for this subject matter eligibility requirement is Section 101, the actual statute does not describe this issue at all. Rather, courts have created this law and the test for patent eligibility, which makes interpreting this issue much more difficult.
The Bitlaw website contains a great deal of information on this topic, which can be found through the Section 101 Index. In some ways, the considerations of subject matter eligibility for AI-related inventions do not differ that much from other software related inventions. Bitlaw provides a lot of Section 101 Guidance on how to analyze these issues, and how to handle rejections made by the Patent Office relating to Section 101.
The USPTO has issued some specific guidance, however, in the form of an example hypothetical. In Example 39, "Method for Training a Neural Network for Facial Detection," the Patent Office provided instructions to its examiners on how to handle certain types of AI-related patent applications. The example describes a hypothetical claim, and then explains how this claim is clearly eligible for patent protection without much detailed analysis. Many patent attorneys have used this example to help convince the Patent Office that patent applications relating to the training of neural networks are eligible for patent protection.
Please see Dan Tysver's bio and contact information if you need any AI-related legal assistance. Dan is a Minnesota-based attorney providing AI advice on intellectual property and litigation issues to clients across the country.