US 12,455,907 B2
Characterization for erroneous artificial intelligence outputs
Galen Rafferty, Mahomet, IL (US); Samuel Sharpe, Cambridge, MA (US); Brian Barr, Schenectady, NY (US); Jeremy Goodsitt, Champaign, IL (US); Austin Walters, Savoy, IL (US); and Kenny Bean, Herndon, VA (US)
Assigned to Capital One Services, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Dec. 5, 2022, as Appl. No. 18/061,689.
Prior Publication US 2024/0184813 A1, Jun. 6, 2024
Int. Cl. G06F 16/3329 (2025.01); G06F 16/35 (2025.01); G06F 40/56 (2020.01)
CPC G06F 16/3329 (2019.01) [G06F 16/35 (2019.01); G06F 40/56 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A method of characterization for erroneous artificial intelligence outputs, comprising:
obtaining data indicating reparations issued by an entity that uses artificial intelligence to provide artificial intelligence outputs in connection with users, the reparations being issued for one or more of the artificial intelligence outputs being erroneous;
determining, using a machine learning model and based on one or more future entity categories in which the entity is to operate, an artificial intelligence reparation characterization for the entity, wherein the artificial intelligence reparation characterization determined using the machine learning model is indicative of an amount of reparations predicted for the entity in connection with uses of the artificial intelligence by the entity, wherein the machine learning model is trained to determine the artificial intelligence reparation characterization based on the data and the one or more future entity categories, and wherein a first node in an input layer of the machine learning model represents one or more historical reparations and a second node in the input layer of the machine learning model represents one or more user sentiments;
rejecting or approving, using the machine learning model, an application for services based on the artificial intelligence reparation characterization; and
transmitting information indicating the artificial intelligence reparation characterization, wherein the information indicates an approval or a rejection of the application for services.