US 12,386,667 B2
Dynamically selecting artificial intelligence models and hardware environments to execute tasks
Ashok Pancily Poothiyot, San Francisco, CA (US); Ali Zafar, Fremont, CA (US); Anthony Penta, Bellevue, WA (US); Stephen Voorhees, Alamo, CA (US); Tim Gasser, Austin, TX (US); Tsung-Hsiang Chang, Bellevue, WA (US); and Geoff Hulten, Lynnwood, WA (US)
Assigned to Dropbox, Inc., San Francisco, CA (US)
Filed by Dropbox, Inc., San Francisco, CA (US)
Filed on Jun. 3, 2024, as Appl. No. 18/732,305.
Claims priority of provisional application 63/623,662, filed on Jan. 22, 2024.
Prior Publication US 2025/0238265 A1, Jul. 24, 2025
Int. Cl. G06F 9/50 (2006.01)
CPC G06F 9/5027 (2013.01) 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving, from a device connected by a network to a content management system, workload data requesting execution of a task using a machine-learning model;
extracting, from the workload data, workload features defining characteristics of the task;
determining task routing metrics for a plurality of hardware environments hosted in respective network environments and a plurality of machine-learning models in respective network environments;
determining a historical quality metric indicating how one or more machine learning models of the plurality of machine-learning models will execute the task based on one or more user feedback metrics; and
selecting, based on an output of a model selection machine-learning model that utilizes the historical quality metric, from the plurality of hardware environments and from the plurality of machine-learning models, a designated hardware environment and a designated machine-learning model for executing the task based on the workload features and the task routing metrics.