US 11,900,325 B2
Utilizing a combination of machine learning models to determine a success probability for a software product
Aditi Kulkarni, Bangalore (IN); Roopalaxmi Manjunath, Bangalore (IN); Sudha Srinivasan, Bangalore (IN); Rajesh Nagarajan, Chennai (IN); Koushik M. Vijayaraghavan, Chennai (IN); Nishanth Kumar, Udupi (IN); Sudhir Hanumanthappa, Bengaluru (IN); Parul Jagtap, Delhi (IN); and Sangeetha Jayaram, Mumbai (IN)
Assigned to Accenture Global Solutions Limited, Dublin (IE)
Filed by Accenture Global Solutions Limited, Dublin (IE)
Filed on Jul. 19, 2021, as Appl. No. 17/379,388.
Prior Publication US 2023/0017316 A1, Jan. 19, 2023
Int. Cl. G06Q 10/10 (2023.01); G06Q 10/0639 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01)
CPC G06Q 10/103 (2013.01) [G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06Q 10/06393 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving, by a device, project management data identifying a project management life cycle associated with development of a software product;
processing, by the device, a first portion of the project management data, with a first plurality of machine learning models, to generate timeliness scores for the software product;
combining, by the device, the timeliness scores to determine an overall timeliness score for the software product;
processing, by the device, a second portion of the project management data, with a second plurality of machine learning models, to generate quality scores for the software product;
combining, by the device, the quality scores to determine an overall quality score for the software product;
processing, by the device, a third portion of the project management data, with a third plurality of machine learning models, to generate product readiness scores for the software product;
combining, by the device, the product readiness scores to determine an overall product readiness score for the software product;
utilizing, by the device, a fourth machine learning model, with the overall timeliness score, the overall quality score, and the overall product readiness score, to generate a success probability for the software product; and
performing, by the device, one or more actions based on the success probability for the software product.