US 11,747,952 B1
Specialization of a user interface using machine learning
David Sander, Newmarket, NH (US); Brian McLaughlin, Hampton, NH (US); Fred Ramberg, North Hampton, NH (US); and Norman DeLuca, Boston, MA (US)
Assigned to Bottomline Technologies Inc., Portsmouth, NH (US)
Filed by Bottomline Technologies (de) Inc., Portsmouth, NH (US)
Filed on Aug. 23, 2019, as Appl. No. 16/549,623.
Application 16/549,623 is a continuation in part of application No. 16/536,754, filed on Aug. 9, 2019, granted, now 11,436,501.
Int. Cl. G06F 3/0481 (2022.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06N 5/025 (2023.01); G06N 20/20 (2019.01)
CPC G06F 3/0481 (2013.01) [G06N 5/025 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01)] 19 Claims
OG exemplary drawing
 
1. An improved method for automatically suggesting optimal actions to a user in a user interface comprising:
receiving a set of input parameters;
accessing a list of possible actions;
filtering the list of possible actions to remove the actions that are not available the user;
looping through the filtered list of possible actions until the filtered list is processed, executing a machine learning model of an optimal user's behavior on each possible action with the set of input parameters to obtain a machine learning score;
storing the machine learning score with the possible action;
once the list of possible actions is processed, sorting the list of possible actions by the machine learning score;
selecting the possible actions with high machine learning scores; offering the user options to perform the possible actions;
wherein the machine learning model is built by iterating through possible rule sets to identify the rule set with a best quality score using a data set of previous user behavior; and
wherein before the looping through the filtered list of possible actions, accessing a list of possible situations; looping through the list of possible situations until the list of possible situations is processed, executing a situations machine learning model of user behavior on each possible situation with the input parameters to obtain a situations machine learning score: storing the situations machine learning score with the possible situation: once the list of possible situations is processed, sorting the list of possible situations by the score: selecting the possible situations with high scores.