US 12,333,577 B2
Automatic rule generation for next-action recommendation engine
Yuxi Zhang, San Francisco, CA (US); Kexin Xie, San Mateo, CA (US); Shrestha Basu Mallick, San Francisco, CA (US); and Darrell Grissen, Boston, MA (US)
Assigned to Salesforce, Inc., San Francisco, CA (US)
Filed by Salesforce, Inc., San Francisco, CA (US)
Filed on Jan. 5, 2024, as Appl. No. 18/405,279.
Application 18/405,279 is a continuation of application No. 17/563,874, filed on Dec. 28, 2021, granted, now 11,900,424, issued on Feb. 13, 2024.
Application 17/563,874 is a continuation of application No. 16/520,556, filed on Jul. 24, 2019, granted, now 11,210,712, issued on Dec. 28, 2021.
Prior Publication US 2024/0144328 A1, May 2, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/02 (2023.01); G06Q 30/0201 (2023.01); G06Q 30/0251 (2023.01)
CPC G06Q 30/0281 (2013.01) [G06Q 30/0201 (2013.01); G06Q 30/0271 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A computer-implemented method using an intent propensity model, the method comprising:
receiving customer data corresponding to a first customer of a plurality of customer, the customer data stored in a memory storage device and including a plurality of historic customer interaction points;
identifying a first customer behavior based on two or more customer interaction points of the plurality of historic customer interaction points;
identifying a goal of the first customer based on the first customer behavior, the goal based on a second customer behavior of a second customer of the plurality of customer;
determining, through use of an intent propensity model, a plurality of customer propensities that the first customer will meet each of a plurality of objectives based on the goal;
assigning a policy from a plurality of policies to the first customer based on scoring each of the plurality of objectives from the plurality of customer propensities, the policy based on a mapping between the customer data and one or more actions of a plurality of actions associated with the policy;
outputting to the first customer using the intent propensity model a recommended next action from the plurality of actions associated with the assigned policy;
receiving a new customer interaction point from the first customer and responsive to the recommended next action; and
iterating the method using the intent propensity model to recommend personalized content and experiences as customer data is updated.