US 11,935,069 B2
Event-driven platform for proactive interventions in a software application
Neha Giri, Mississauga (CA); Hemanth Nellitheertha, Bengaluru (IN); Bhargava Narayana, Bengaluru (IN); Manish Jain, Bengaluru (IN); Divya Kumar, Bengaluru (IN); and Arun Kumar A, Bengaluru (IN)
Assigned to Inuit, Inc., Mountain View, CA (US)
Filed by INTUIT INC., Mountain View, CA (US)
Filed on Sep. 28, 2021, as Appl. No. 17/487,250.
Prior Publication US 2023/0095109 A1, Mar. 30, 2023
Int. Cl. G06Q 30/016 (2023.01); G06F 9/451 (2018.01); G06N 20/00 (2019.01); G06Q 30/0207 (2023.01)
CPC G06Q 30/016 (2013.01) [G06F 9/453 (2018.02); G06N 20/00 (2019.01); G06Q 30/0224 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A method for proactive intervention in a software application, comprising:
providing, by a proactive intervention system related to an application, event information of a plurality of events as inputs to a first machine learning model, wherein:
the event information comprises clickstream data related to use of the application and contextual information associated with the plurality of events;
the contextual information comprises user attributes; and
the first machine learning model has been trained through a supervised learning process to recognize that certain events are correlated with users abandoning use of the application;
receiving, from the first machine learning model in response to the inputs, abandonment confidence scores for the plurality of events, wherein each of the abandonment confidence scores indicates a likelihood that a user will abandon the application;
assigning, by the proactive intervention system, priorities to the plurality of events based on the abandonment confidence scores;
selecting, by the proactive intervention system, an event of the plurality of events for processing based on the priorities assigned to the plurality of events;
providing, by the proactive intervention system, one or more inputs to a second machine learning model, the one or more inputs including the event information and the abandonment confidence scores, wherein the second machine learning model has been trained through a supervised learning process by which parameters of the second machine learning model were iteratively adjusted based on a training data set comprising attributes of historical events associated with labels indicating that certain proactive interventions are relevant to the historical events;
determining, by the proactive intervention system, a proactive intervention based on a proactive intervention confidence score with respect to the proactive intervention that is output by the second machine learning model in response to the one or more inputs;
determining, by the proactive intervention system, that the proactive intervention can presently be provided based on intervention availability data;
providing, by the proactive intervention system, the proactive intervention via a user interface associated with the application;
receiving user feedback with respect to the proactive intervention, wherein the second machine learning model is re-trained based on the user feedback to generate a re-trained second machine learning model; and
using the re-trained second machine learning model to determine a subsequent proactive intervention.