US 12,332,767 B2
Techniques for automatically triaging and describing issues detected during use of a software application
Renzo F. Lucioni, Somerville, MA (US); and Nikhil S. Phatak, Somerville, MA (US)
Assigned to LogRocket, Inc., Boston, MA (US)
Filed by LogRocket, Inc., Boston, MA (US)
Filed on Jan. 25, 2024, as Appl. No. 18/422,216.
Claims priority of provisional application 63/596,215, filed on Nov. 3, 2023.
Claims priority of provisional application 63/441,701, filed on Jan. 27, 2023.
Prior Publication US 2024/0256424 A1, Aug. 1, 2024
Int. Cl. G06F 11/3604 (2025.01); G06F 11/362 (2025.01); G06F 11/3698 (2025.01)
CPC G06F 11/3608 (2013.01) [G06F 11/366 (2013.01); G06F 11/3698 (2025.01)] 21 Claims
OG exemplary drawing
 
1. A system for automatically generating natural language descriptions of issues that occur during interactions of users with a software application, the system comprising:
a processor; and
a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, cause the processor to:
access data collected during at least one software application session in which at least one user was interacting with the software application and in which an issue occurred;
generate, using the data collected during the at least one software application session, at least one representation of the at least one software application session, wherein the at least one representation of the at least one software application session indicates a sequence of events that occurred in the at least one software application session; and
process, using a description generation module, the at least one representation of the at least one software application session using an automated series of input/output exchanges between the description generation module and a generative machine learning model to obtain the natural language description of the issue that occurred in the at least one software application session, the processing comprising:
generate an input to the generative machine learning model that includes at least a portion of the at least one representation of the at least one software application session;
provide the input to the generative machine learning model to obtain an output;
generate a subsequent input to the generative machine learning model by processing the output;
provide the subsequent input to the generative machine learning model to obtain a subsequent output; and
generate the natural language description of the issue using the subsequent output obtained from the generative machine learning model.