US 12,339,929 B2
Machine learned feature recommendation engine in a digital transaction management platform
Andrew James Ashlock, San Francisco, CA (US); Ronald Hirson, San Francisco, CA (US); and Mark Douglas Belanger, Woodinville, WA (US)
Assigned to Docusign, Inc., San Francisco, CA (US)
Filed by DocuSign, Inc., San Francisco, CA (US)
Filed on Nov. 17, 2023, as Appl. No. 18/512,382.
Application 18/512,382 is a continuation of application No. 16/870,557, filed on May 8, 2020, granted, now 11,822,622, issued on Nov. 21, 2023.
Prior Publication US 2024/0086496 A1, Mar. 14, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 18/22 (2023.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06N 5/025 (2023.01); G06N 20/00 (2019.01)
CPC G06F 18/22 (2023.01) [G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06N 5/025 (2013.01); G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
identifying, using at least one processor, a first entity and a second entity associated with the first entity in a plurality of entities;
determining, using the at least one processor, one or more characteristics of at least one of the first entity and the second entity and a historical usage of one or more features of an online document system by at least one of the first entity and the second entity;
applying, using the at least one processor, a machine learning model to the one or more characteristics and the historical usage of the one or more features to identify at least one feature in the one or more features for a recommendation, the at least one feature is identified based on at least one mapping between the one or more characteristics and the one or more features; and
generating, using the at least one processor, the recommendation to the first entity, the recommendation including the at least one feature.