US 12,229,038 B2
Using machine learning model to make action recommendation to improve performance of client application
Jess Robert Kerlin, Walnut Creek, CA (US); Eric Antoine MacKinnon, Henderson, NV (US); and Paul Ernest Stolorz, Los Altos, CA (US)
Assigned to Data.ai Inc., San Francisco, CA (US)
Filed by Data.ai Inc., San Francisco, CA (US)
Filed on Apr. 21, 2023, as Appl. No. 18/305,240.
Application 18/305,240 is a continuation of application No. 17/540,128, filed on Dec. 1, 2021, granted, now 11,656,969.
Application 17/540,128 is a continuation of application No. 17/346,117, filed on Jun. 11, 2021, granted, now 11,221,937, issued on Jan. 11, 2022.
Prior Publication US 2023/0267062 A1, Aug. 24, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 11/34 (2006.01); G06N 20/00 (2019.01)
CPC G06F 11/3452 (2013.01) [G06F 11/3495 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
inputting both a target performance metric of a client application and a plurality of features of the client application into a machine learning model;
receiving as output from the machine learning model one or more sets of target features different from the plurality of features of the client application;
ranking the one or more sets of target features output from the machine learning model based on sets of distance weights representing distances between the plurality of features of the client application and the one or more sets of target features;
determining a set of recommended actions based on the ranking; and
providing, for display at a client device, the set of recommended actions to be performed on the client application.