US 12,079,585 B1
Scalable systems and methods for discovering and summarizing test result facts
Dustin Garvey, Exeter, NH (US); Janet Muto, Boston, MA (US); Nitzan Shaer, Newton, MA (US); Shannon Walsh, Boston, MA (US); Alexa Stewart, Andover, MA (US); Andrea Paola Aguilera Garcia, Cambridge, MA (US); Kim Coccoluto, Wakefield, MA (US); Sara Peters, Syracuse, NY (US); Ruthie McCready, Austin, TX (US); Kelly Lyons, Boston, MA (US); Melany Carvalho, Pawtucket, RI (US); Everett Granger, Virginia Beach, VA (US); Julia McCarthy, Boston, MA (US); Frank Chiang, Boston, MA (US); Alexander Barza, Cambridge, MA (US); and Hannah Sieber, Mendon, MA (US)
Assigned to Wevo, Inc., Boston, MA (US)
Filed by Wevo, Inc., Boston, MA (US)
Filed on Apr. 24, 2023, as Appl. No. 18/306,028.
Int. Cl. G06F 40/40 (2020.01)
CPC G06F 40/40 (2020.01) 18 Claims
OG exemplary drawing
 
1. A method comprising:
receiving a set of results for a user experience test;
generating a set of clusters, wherein each respective cluster includes at least one respective result from the set of results for the user experience test and represents a particular topic;
for each cluster:
generating a set of segments, wherein each segment includes a subset of the set of results in the cluster and is generated based at least in part on test element type;
instantiating different finding generators for different segments in the set of segments, wherein a machine learning model is used by at least one of the finding generators; and
generating, with the different finding generators, a respective finding summary for each segment in the cluster based at least in part on the test element type associated with the segment, wherein the finding summaries comprise natural language text readable by humans, and wherein the machine learning model extrapolates from learned patterns to distinguish between high-quality and low-quality results for the user experience test; and
adjusting, via a training model, one or more internal parameters of the machine learning model used by at least one of the finding generators to generate improved finding summaries, wherein the training model retrains the machine learning model using new input data that is local in time such that the model is no longer trained on data that is older than a threshold age.