This article explores USPTO handling of Alice issues involving artificial intelligence and machine learning through a sampling of recent Patent Trial and Appeal Board (PTAB) decisions. See Alice Corp. v. CLS Int’l, 134 S. Ct. 2347 (2014). Some decisions dutifully applied USPTO guidelines on subject matter eligibility, including Example 39 thereof, to resolve appeal issues brought to the PTAB. In one case, the PTAB sua sponte offered eligibility guidance even with no Alice appeal issue before it. These decisions inform strategies to optimize patent drafting and prosecution for artificial intelligence and machine learning related inventions.
“Generic Machine Learning Algorithm”
In Ex parte Hussain, Appeal No. 2020-005406 (PTAB Feb. 18, 2021), the PTAB considered the subject matter eligibility of claims reciting a “machine learning algorithm” in relation to mitigation of risk of consumer default on an online transaction. Representative claim 1 recited as follows:
- A computer-implemented method, comprising: under the control of one or more computer systems that execute instructions,providing executable instructions to a client computing device associated with a user that, as a result of being executed by the client computing device, causes the client computing device to:
collect client data that includes:
personally identifiable information about the user; an identifier associated with the client computing device; and a measurement captured by the client computing device associated the user interacting with the client computing device, the measurement including:
an action performed by the user to an object displayed in a user interface of the client computing device; an identity of the object; and a time value indicating a time at which the action was performed to the object; and provide the client data to the one or more computer systems;
obtaining stored transactional data associated with one or more previous transactions involving the user;
obtaining verification data verifying that the personally identifiable information is accurate;
transforming the stored transactional data, the verification data, and the client data that includes the measurement into a set of variable values usable as input into a machine learning algorithm that is trained to infer characteristics about the user from the set of variable values;
obtaining, as a result of inputting the set of variable values into the machine learning algorithm, a fidelity score output by the machine learning algorithm; and
based at least in part on the fidelity score and without obtaining additional information about the user from a third party:
determining a payment or credit option to display in the user interface for a current transaction; and updating, contents of the user interface to provide the payment or credit option.
