US 11,855,860 B1
Domain-specific generative machine learning models
Ajoy Kumar, Santa Clara, CA (US); Himanshu Singhvi, Pune (IN); and Priya Saurabh Talwalkar, Pune (IN)
Assigned to BMC Software, Inc., Houston, TX (US)
Filed by BMC Software, Inc., Houston, TX (US)
Filed on Mar. 31, 2023, as Appl. No. 18/194,204.
Int. Cl. H04L 41/5074 (2022.01); H04L 41/16 (2022.01)
CPC H04L 41/5074 (2013.01) [H04L 41/16 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable storage medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to:
receive a plurality of resolved incident tickets of an incident domain, each resolved incident ticket having a worklog providing a history of actions taken during attempts to resolve a corresponding resolved incident and a resolution having at least one resolution statement for the corresponding resolved incident;
execute an iterative processing of the plurality of resolved incident tickets to obtain processed incident tickets, including:
(a) processing each resolution statement of the resolution with at least one domain-specific statement classifier that is specific to the incident domain to either discard or retain a classified resolution statement,
(b) processing each retained classified resolution statement in conjunction with the worklog to determine whether to discard or retain the resolved incident,
(c) providing an updated resolution for the resolved incident when the resolved incident is retained, and
(d) adding the resolved incident with the updated resolution to the processed incident tickets; and
train at least one machine learning model to process a new incident ticket including generating a new resolution statement using a new worklog of the new incident ticket, using the processed incident tickets.