US 12,216,554 B1
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. 18/732,320.
Claims priority of provisional application 63/623,662, filed on Jan. 22, 2024.
Int. Cl. G06F 9/46 (2006.01); G06F 9/50 (2006.01); G06F 11/14 (2006.01); G06F 11/20 (2006.01)
CPC G06F 11/2023 (2013.01) [G06F 9/5005 (2013.01); G06F 11/1476 (2013.01); G06F 11/2038 (2013.01); G06F 2209/501 (2013.01); G06F 2209/503 (2013.01); G06F 2209/509 (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;
selecting, based on the workload features, a primary machine-learning model for executing the task and a fallback machine-learning model for executing the task if the primary machine-learning model is unavailable; and
based on detecting that the primary machine-learning model is unavailable prior to executing the task, providing the workload data to a computing environment of the fallback machine-learning model for executing the task.