US 11,861,518 B2
High fidelity predictions of service ticket escalation
Zach Riddle, San Jose, CA (US); Andrew Langdon, San Jose, CA (US); Poonam Rath, San Jose, CA (US); Charles Monnett, San Jose, CA (US); and Lawrence Spracklen, San Jose, CA (US)
Assigned to SupportLogic, Inc., San Jose, CA (US)
Filed by SupportLogic, Inc., San Jose, CA (US)
Filed on Jun. 29, 2020, as Appl. No. 16/914,935.
Application 16/914,935 is a continuation in part of application No. 16/712,799, filed on Dec. 12, 2019.
Claims priority of provisional application 62/869,889, filed on Jul. 2, 2019.
Prior Publication US 2021/0004706 A1, Jan. 7, 2021
Int. Cl. G06N 20/00 (2019.01); G06Q 10/0631 (2023.01); G06F 40/40 (2020.01); G06Q 10/0637 (2023.01); G06Q 10/0635 (2023.01); G06Q 30/01 (2023.01); G06F 40/289 (2020.01); G06N 7/01 (2023.01); G06Q 10/20 (2023.01); G06Q 10/10 (2023.01); G06Q 30/016 (2023.01)
CPC G06N 7/01 (2023.01) [G06F 40/289 (2020.01); G06F 40/40 (2020.01); G06N 20/00 (2019.01); G06Q 10/0635 (2013.01); G06Q 10/06312 (2013.01); G06Q 10/06375 (2013.01); G06Q 10/063114 (2013.01); G06Q 10/10 (2013.01); G06Q 10/20 (2013.01); G06Q 30/01 (2013.01); G06Q 30/016 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A system for high fidelity predictions of service ticket escalation, the system comprising:
one or more processors; and
a non-transitory computer readable medium storing a plurality of instructions, which when executed, cause the one or more processors to:
derive a training set change factor for a periodic observation that service ticket interactions associated with the training set service ticket include a response to a request from the training set product user which natural language processing detects as a non-substantive response, such that the service ticket interactions continue to lack a substantive response to a request from the training set product user;
train, using the training set service ticket, the training set change factor, and a change-based machine-learning model to predict a change-based training probability that the training set product user escalated service for the training set service ticket;
derive a change factor for a periodic observation that service ticket interactions associated with the service ticket includes a response to a request from the product user which natural language processing detects as a non-substantive response, such that the service ticket interactions continue to lack a substantive response to a request from the product user;
predict, by applying the change-based machine-learning model to the service ticket and the change factor, a change-based probability that the product user escalates service for the service ticket;
output the change-based probability; and
retrain, using data which includes the service ticket, the change factor, and the change-based probability, the change-based machine-learning model to predict a subsequent change-based training probability that a subsequent product user escalated service for a subsequent service ticket.