US 12,112,388 B2
Utilizing a machine learning model for predicting issues associated with a closing process of an entity
Chiranjit Seal, Kolkata (IN); Bula Roy, Pune (IN); Atrayee Chatterjee, Pune (IN); Kaushik Dey, Howrah (IN); and Punitha Nithyanandam, Bangalore (IN)
Assigned to Accenture Global Solutions Limited, Dublin (IE)
Filed by Accenture Global Solutions Limited, Dublin (IE)
Filed on Nov. 30, 2020, as Appl. No. 17/247,092.
Prior Publication US 2022/0172298 A1, Jun. 2, 2022
Int. Cl. G06F 16/25 (2019.01); G06F 16/23 (2019.01); G06F 16/28 (2019.01); G06F 18/214 (2023.01); G06F 21/62 (2013.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06Q 10/0631 (2023.01); G06Q 10/0639 (2023.01); G06Q 40/08 (2012.01); G06Q 40/12 (2023.01)
CPC G06Q 40/12 (2013.12) [G06F 18/214 (2023.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06Q 10/063114 (2013.01); G06Q 10/06393 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
identifying, by a device, tasks relevant to closing processes for an entity during a period of time;
generating, by the device, a request for historical closing data associated with the tasks relevant to the closing processes;
providing, by the device, the request to a server device associated with the entity;
receiving, by the device, the historical closing data based on providing the request to the server device,
wherein the historical closing data comprises information regarding historical issues that caused one or more historical tasks to be delayed including descriptions of historical reasons for historical delays and information identifying a measure of severity of the historical issues;
identifying, by the device, a trend with respect to the one or more historical tasks based on sorting the one or more historical tasks based on amounts of delays associated with the historical delays;
training, by the device, a machine learning model, based on the historical closing data, to generate a trained machine learning model,
wherein the machine learning model is trained to identify structural or organizational changes associated with reasons for the historic delays from the historical closing data, and
wherein the machine learning model is trained to identify particular tasks that are associated with delays and particular periods of time associated with lengths of the delays;
receiving, by the device, current closing data associated with tasks relevant to a current closing process;
processing, by the device, the current closing data, with the trained machine learning model, to predict issue data identifying at least one potential issue associated with the current closing process;
providing, by the device, the issue data to a user device associated with the at least one potential issue;
generating, by the device, a modification to the current closing process based on the issue data; and
causing, by the device, the server device to implement the modification to the current closing process.