US 12,423,656 B2
Machine learning-based anomaly detection and action generation system for software development lifecycle management
Mayank Vinayaka, Irving, TX (US); Ryan Peterman, Jacksonville, FL (US); Badari Narayana Shanka Prasad, Irving, TX (US); Richard Lawton, Irving, TX (US); Vitthal Ramling Betgar, New York, NY (US); Adar Danait, New York, NY (US); Balaji Kumar, New York, NY (US); Robin J. Kurian, New York, NY (US); Maneet Sharma, New York, NY (US); and Anantha Veerasami, New York, NY (US)
Assigned to CITIBANK, N.A., , NY (US)
Filed by Citibank, N.A., New York, NY (US)
Filed on Apr. 4, 2025, as Appl. No. 19/171,152.
Application 19/171,152 is a continuation in part of application No. 18/925,536, filed on Oct. 24, 2024, granted, now 12,288,192.
Application 18/925,536 is a continuation in part of application No. 18/380,114, filed on Oct. 13, 2023, granted, now 12,236,400, issued on Feb. 25, 2025.
Application 18/380,114 is a continuation of application No. 18/124,870, filed on Mar. 22, 2023, granted, now 11,797,936, issued on Oct. 24, 2023.
Prior Publication US 2025/0232259 A1, Jul. 17, 2025
Int. Cl. G06Q 10/06 (2023.01); G06Q 10/0631 (2023.01); G06Q 10/10 (2023.01); G06Q 10/101 (2023.01); G06Q 10/067 (2023.01)
CPC G06Q 10/103 (2013.01) [G06Q 10/063114 (2013.01); G06Q 10/067 (2013.01)] 20 Claims
OG exemplary drawing
 
1. One or more non-transitory computer-readable media storing instructions, which when executed by at least one processor, perform operations comprising:
generating a system data stream for a software development lifecycle (SDLC) management platform that stitches together data from a plurality of computing systems;
receiving a first machine learning model trained based on past system data streams to generate trend information for one or more metrics and identify one or more anomalies in the trend information for an input user story identifier;
receiving a second machine learning model trained based on past trend information to generate one or more dynamic thresholds for the one or more anomalies in the trend information for the input user story identifier;
receiving an adaptive model trained on the past system data streams and the past trend information to generate one or more recommended actions to address the one or more anomalies in the trend information for the input user story identifier;
generating a query to the first machine learning model for each user story identifier of a plurality of user stories corresponding to the SDLC management platform;
based on output from the first machine learning model including (i) trend information for the one or more metrics and (ii) the one or more anomalies in the trend information for each user story identifier, generating a graphical user interface configured to be displayed at a client device, wherein the graphical user interface includes information for each user story of the plurality of user stories and corresponding progress tracking indicia;
based on output from the second machine learning model including the one or more dynamic thresholds for the one or more anomalies in the trend information for a corresponding user story identifier, determining an anomaly from the one or more anomalies that satisfies a dynamic threshold corresponding to the anomaly;
processing, using the adaptive model, the anomaly in the trend information for the corresponding user story identifier, to generate one or more recommended actions to address the anomaly; and
based on output from the adaptive model including one or more recommended actions to address the anomaly, generating for inclusion in the graphical user interface the information for each user story of the plurality of user stories and corresponding progress tracking indicia and the one or more recommended actions to address the anomaly.