Keys to AI IP Strategy: A Comprehensive Framework

23 June 2020 Privacy, Cybersecurity & Technology Law Perspectives Blog
Authors: Shabbi S. Khan Nikhil T. Pradhan

This article outlines a framework for creating valuable patents for protecting AI technologies, as part of a series that covers topics including why to invest in patents for AIhow to overcome the biggest challenges in patenting AI, and business factors to consider in building your patent portfolio. The framework highlights several complementary approaches for claiming the inventions underlying any particular AI technology, and answers the following questions that characterize useful patent claims:

  1. does the claim prevent competitors from performing AI functionality that provides a competitive advantage in your market
  2. is the claim resistant to invalidity challenges
  3. can infringement of the claim be detected

At a high level, most, if not all, AI technologies can be broken down into the inputs provided to an AI algorithm, such as a machine learning algorithm, how the algorithm processes the inputs (which may often be seen as a black box), and the outputs of the algorithm. As shown below, patent claims can generally be prepared to cover at least three categories of inventions for AI technologies that map to the input-processing-output breakdown.

Input Processing  Output 
Training Data Generation: where a machine learning model is used, what steps are taken to make the training data more useful -- which results in a more useful model -- such as filtering the data, reserving data for validation, automating the generation of training data or incorporating user feedback? Model Training: where a machine learning model is used, how is the model being trained and what is the value-add generated by using a trained model as opposed to other approaches, such as rule-based or engineered approaches? Insights and Actions: what is the outcome of using the AI solution? What patterns or new knowledge are identified? How is a physical machine, such as a medical device, industrial automation robot, or autonomous vehicle controlled using information generated by the AI solution?

The framework outlined below applies this breakdown in a manner suited for machine learning technologies that train models to generate useful insights and actions (but can be readily adjusted to address AI solutions that do not necessarily use machine learning models, such as heuristics/rule based engines):

Machine Learning Patent Protection Framework

Category Why  How  Detectability 
Insights/Actions  This is the real world result and value add  How does the action improve the underlying technology?

Are you faster/more accurate/able to respond to situations before they occur?
Focus on inputs and outputs while varying the granularity of claims from the processing components up to the system in which the AI processing is implemented

Ideally can treat the algorithm itself as a black box
Model Training  This is where you enable the technical improvements that make your system have a competitive advantage  Does training the model enable your system to perform functions it could not previously?

Does training the model enable your system to be faster or more accurate than other ML approaches, human approaches or rules-based approaches?
Focus on input data including the source of the data and the combination of input data parameters

Capture the most likely types of models that could be used using varying claim scope 
Training Data Generation and Pre-processing Remedy for “garbage in garbage out” problem

Automation of the pre-processing to make implementing AI solutions faster/more efficient/use fewer computational resources 
Emphasize computer steps used to improve the training data

Identify unique approaches for manipulating baseline data into data that may be expressed in different forms relevant to the technology area, such as filtering, changing dimensionality, automated annotation, identifying a subset of parameters that are more significant than others for the model, etc. 
Focus on input data including the source of the data and the combination of input data parameters

Capture the right types and scope of data, sift through to get the optimal amount of data, and properly label the data to teach your system the correct way for insights/actions 

By using this kind of framework, you can identify multiple ways for patenting your AI technology, resulting in a stronger patent portfolio that will be easier to enforce and more difficult for competitors to design around.  The next articles in this series will dig into specific tactics to help achieve these goals when your applications are examined.

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