US 12,223,254 B2
Using machine learning to predict performance of secure documents
Soroush Salehian, Kirkland, WA (US); William Pittman, Seattle, WA (US); John Barcellos, Seattle, WA (US); Santiago Szuchmacher, Seattle, WA (US); Chris Marshall, Shoreline, WA (US); Jeong Woo Chang, Mercer Island, WA (US); and Dylan Ray Häs, Seattle, WA (US)
Assigned to Docusign, Inc., San Francisco, CA (US)
Filed by DocuSign, Inc., San Francisco, CA (US)
Filed on Sep. 27, 2023, as Appl. No. 18/476,060.
Application 18/476,060 is a continuation of application No. 17/682,859, filed on Feb. 28, 2022, granted, now 11,809,805.
Application 17/682,859 is a continuation of application No. 17/067,558, filed on Oct. 9, 2020, granted, now 11,301,616, issued on Mar. 23, 2022.
Prior Publication US 2024/0020459 A1, Jan. 18, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 40/00 (2020.01); G06F 18/21 (2023.01); G06F 21/62 (2013.01); G06F 40/106 (2020.01); G06F 40/186 (2020.01); G06F 40/216 (2020.01); G06N 20/00 (2019.01); G06V 30/40 (2022.01)
CPC G06F 40/106 (2020.01) [G06F 18/2193 (2023.01); G06F 21/6209 (2013.01); G06F 40/186 (2020.01); G06F 40/216 (2020.01); G06N 20/00 (2019.01); G06V 30/40 (2022.01)] 15 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
detecting, using at least one processor, a transmission of a request from a requesting computing device to a recipient computing device to perform one or more tasks associated with a secure document;
identifying, using the at least one processor, based on the request in the detected transmission, one or more machine learned models in a plurality of machine learned models, the one or more machine learned models have been trained on one or more features of one or more historical secure documents and task completion data associated with the one or more historical secure documents, and determining, using the identified one or more machine learned models, one or more probabilities associated with a failure of the recipient computing device to complete performance of the one or more tasks, wherein the determined one or more probabilities are indicative of a failure to complete performance of the one or more tasks based on at least one of the following: a type of task in the one or more tasks, a time of transmission of the request, a time associated with performance of at least one task in the one or more tasks, and any combination thereof;
generating, using at least one processor, based on the determined one or more probabilities, one or more feature modification recommendations, each feature modification recommendation in the one or more feature modification recommendations being associated with a value of probability indicative of an improved likelihood of a successful completion of performance of the one or more tasks by the recipient computing device; and
presenting, using at least one processor, the generated one or more feature modification recommendations and associated respective values of probability on a graphical user interface of the requesting computing device.