Artificial Intelligence

 

Innovations involving artificial intelligence (AI) and machine learning (ML) are being developed at an ever-accelerating pace. For example, Figure 1 illustrates that the number of patent applications published by the United States Patent and Trademark Office (USPTO) including the phrases "artificial intelligence" and "machine learning" has increased from 373 publications in 2009 to 6,476 publications in 2019.

Source: Google Patents, https://patents.google.com

Practitioner Perspective: Drafting Effective AI/ML-based Patent Applications

From the outset, practitioners should gather information on the novel, non-obvious aspects of the given invention including, but not limited to:

  • Input data preparation: How is data gathered, pre-processed, handled, or parsed upon use by the AI/ML model? Is the data obtained from a specific type of sensor (e.g., a camera or radar device)? What does the data represent (e.g., letters, words, Boolean values)?
  • Model structure: Does the model have specific non-generic features (e.g., a neural network with non-conventional number of nodes at given layers, multiple hidden layers, etc.), and how is the input data mapped to this specific structure?
  • Training phase process: How is the model trained, and in what manner (e.g., tagged input-output pairs, unsupervised learning, etc.)?
  • Execution phase process: What weights are used with respect to what variables? What advantages result from execution of the AI/ML model?
  • Output data post-processing / analysis: How are the outputs utilized and what do they represent (e.g., classification, recommendation, likelihoods, etc.)? Is the data used to control a specific device (e.g., a speech synthesizer or autonomous vehicle)?
  • Locus of AI/ML processing: Is the substantive computing performed locally (e.g., "on the edge"), in the cloud, in multiple locations, and/or elsewhere?
  • AI/ML-based hardware: Does the innovation utilize AI/ML-specific integrated circuits, such as AI/ML -optimized graphics processing units (GPUs)? How does the model structure map to such hardware?

Next, when drafting AI/ML-centric patent claims, practitioners should:

  • Include a "patentable hook" in each independent claim, which hopefully relates back to the AI/ML-based nature of the invention.
  • Separate training phase processes from execution phase processes by utilizing independent claim families directed to the respective methods.
  • Try to incorporate as much physical structure (e.g., controller, computation unit, circuits, etc.) as possible into the claims to obviate issues with 35 U.S. § 101 (patentable subject matter). This structure can include both special hardware used for the training and execution phases, as well as physical devices that provide input data or receive output data.
  • Utilize patent analytics by "testing" sample claim language to predict art unit assignments and iteratively adjust claim terms to actively avoid business method type art units (e.g., art unit 3600) or other low-allowance-rate art units.

Breaking the patent specification into multiple sections that correspond to the major elements of the AI/ML-based invention (e.g., input data preparation, model structure, training phase, etc.) can compartmentalize the disclosure and may help ensure that each novel, non-obvious detail is described thoroughly. Patent drawings should expressly illustrate each substantive claim term in a schematic-type diagram or a method flowchart.

Patent Owner Perspective: Developing an Intentional AI/ML-based Patent Portfolio Strategy

Building and maintaining an AI/ML-based patent portfolio can be expensive and time-consuming. When an invention satisfies many of the factors in the checklist below, it may indicate that preparing and filing a patent application is in line with the company's interests.

Business and Patent Portfolio Goals

First, consider the invention and how it relates to the business and patent portfolio goals of the company.

  • Consider key industry players (competitors, partners, customers). Is the invention directed to technology related to key industry players? Is anyone outside of your company using the invention?
  • Consider this AI/ML-based invention with respect to your own company. Is the invention directed to a fundamental/core technology and/or product of your company? Is the invention currently in use at your company?
  • Consider your company's investment in this AI/ML-based invention. Has a substantial amount of money or employee hours been invested in research and development of the invention?
  • Consider the impact of the AI/ML-based invention. Does the invention provide a disruptive solution to an existing problem in industry (e.g., the invention reduces power usage by 10%)?
  • Consider the "shelf life" of the AI/ML-based invention. Short or long product life?
  • Consider publicizing the AI/ML-based invention. Are you okay with making the invention available to public? Weigh patent rights versus trade secret protection.
  • Consider patent ownership. Will the company be able to claim ownership over the patent (jointly developed)? Does the AI/ML-based code include third-party code or open-source code?
  • Consider the potential market for this patent and monetization potential. Is there potential for licensing revenue or sale of the patent (and return license)? Is there interest in enforcing the patent?
  • Consider copying and reverse-engineering of the invention. Is the invention difficult to reverse-engineer (e.g., an AI/ML-based application running on a private cloud server)?
  • Consider international market/competitors. Should you protect the invention in foreign jurisdictions?

Patent Law Considerations

Second, consider the AI/ML-based invention and whether legal requirements to obtain a patent can be satisfied.

  • Consider the prior art. Is the invention substantially different than conventional systems (e.g., neural network with novel structure or novel input data mapping)?
  • Consider potential claim scope in light of the prior art. Would possible claim scope be too narrow and hard to enforce or easy for a competitor to design around?
  • Consider enforcement of patent rights. Is it easy for you to identify when someone is using the invention?
  • Consider patent eligibility. Is the invention "directed to" laws of nature, mathematical theories, etc.? An algorithm (e.g., a neural network) in and of itself is not patentable subject matter, but an algorithm utilized in a specific method or system could represent patentable subject matter.
  • Consider whether the invention is sufficiently developed. Is the invention past "idea" stage (e.g., AI/ML-based code has been developed and maybe implemented in tests, training phase completed)? When test results exist, consider whether to include them in a patent application.

 

 

Aaron Gin, Ph.D. is a partner with McDonnell Boehnen Hulbert & Berghoff LLP. Dr. Gin has broad experience in preparing and prosecuting U.S. and foreign applications for patents and trademarks. He provides advice in support of patent validity, infringement, patentability analyses, and litigation matters in the electrical and computing technology areas.

Michael S. Borella is a partner with McDonnell Boehnen Hulbert & Berghoff LLP and serves as a Co-Chair of the firm's Software & Business Methods Practice Group. Dr. Borella provides legal and technological advice in support of validity, infringement, patentability analyses, and litigation matters.

Joseph A. Herndon is a partner with McDonnell Boehnen Hulbert & Berghoff LLP and serves as a Co-Chair of the firm's Software & Business Methods Practice Group. Mr. Herndon's prosecution experience includes all phases of U.S. and foreign patent and trademark prosecution, client counseling, due diligence, and opinion work regarding validity, infringement, and enforceability of patents.