US 11,860,968 B2
System and method for integrating user feedback into website building system services
Hana Siani Cohen, Holon (IL); Oded Nachshon, Ramat Gan (IL); Noa Stanger, Shimshit (IL); Shai Dekel, Arsuf (IL); Meir Perez, Modi'in-Maccabim-Reut (IL); and Hila Gat, Tel Aviv (IL)
Assigned to Wix.com Ltd., Tel Aviv (IL)
Filed by Wix.com Ltd., Tel Aviv (IL)
Filed on Mar. 14, 2022, as Appl. No. 17/693,485.
Application 17/693,485 is a continuation of application No. 16/878,831, filed on May 20, 2020, granted, now 11,275,815.
Claims priority of provisional application 63/027,369, filed on May 20, 2020.
Claims priority of provisional application 62/970,034, filed on Feb. 4, 2020.
Claims priority of provisional application 62/905,450, filed on Sep. 25, 2019.
Claims priority of provisional application 62/853,191, filed on May 28, 2019.
Prior Publication US 2022/0197969 A1, Jun. 23, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 3/045 (2006.01); G06F 16/958 (2019.01); G06F 11/34 (2006.01); G06N 20/20 (2019.01); G06N 3/045 (2023.01)
CPC G06F 16/972 (2019.01) [G06F 11/3438 (2013.01); G06N 3/045 (2023.01); G06N 20/20 (2019.01)] 16 Claims
OG exemplary drawing
 
1. A website building system (WBS), the system comprising:
a database storing at least websites of a plurality of users of said WBS, and components of said websites; and
a processor implementing at least one dynamically updatable machine learning
model suitable for at least one specific activity related to said WBS and to provide at least one system suggestion to said users related to said at least one specific activity;
wherein said at least one specific activity related to said WBS comprises at least one multi-component task for handling of websites within said WBS;
wherein at least one machine learning model is trained using at least different object types and feedback types;
a feedback system to dynamically update said at least one machine learning model using implicit feedback about editing behaviors within said WBS and explicit feedback to suggestions and at least one proposal from at least said users, said feedback system to evaluate a response quality of said feedback and to analyze both said explicit and implicit feedback to determine which of said at least one machine learning model to update.