US 12,271,908 B2
Automated system for customer support
Radu Marinescu, Dublin (IE); Akihiro Kishimoto, Setagaya (JP); and Yufang Hou, Dublin (IE)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Jul. 8, 2021, as Appl. No. 17/370,092.
Prior Publication US 2023/0008218 A1, Jan. 12, 2023
Int. Cl. G06Q 30/016 (2023.01); G06F 40/205 (2020.01); G06F 40/279 (2020.01); G06N 20/00 (2019.01); G06Q 10/0631 (2023.01); G06Q 10/0633 (2023.01); G06Q 10/10 (2023.01); G06F 16/23 (2019.01)
CPC G06Q 30/016 (2013.01) [G06F 40/205 (2020.01); G06F 40/279 (2020.01); G06N 20/00 (2019.01); G06Q 10/06316 (2013.01); G06Q 10/0633 (2013.01); G06Q 10/103 (2013.01); G06F 16/23 (2019.01)] 18 Claims
OG exemplary drawing
 
1. A computer-implemented method for an automated information technology (“IT”) support system, the computer-implemented method comprising:
receiving, by one or more processors, a set of sentences extracted from a natural language conversation occurring between an IT support system and a user through a channel used to log an IT support ticket;
extracting, by the one or more processors, an initial state and a goal state from the set of sentences using a Natural Language Classifier, wherein-the Natural Language Classifier evaluates the set of sentences against a plurality of support vector machines and a convolutional neural network based on a first deep learning model;
extracting, by the one or more processors, one or more actions from the set of sentences;
converting, by the one or more processors, the one or more actions into one or more into corresponding planning design description language (“PDDL”) text formal actions;
creating, by the one or more processors, a formal planning model incorporating the extracted initial state, the goal state, and the one or more formal actions, based on the converted PDDL text;
determining, by the one or more processors, whether at least one of the one or more formal actions are not complete based on a first machine learning model;
responsive to determining the at least one of the one or more formal actions are not complete, completing, by the one or more processors, the one or more formal actions with one or more missing parts using the first machine learning model enabled to predict the one or more missing parts of the at least one of the one or more formal actions that are not complete to produce one or more complete formal actions;
generating, by the one or more processors, an executable plan to move from the initial state to the goal state by mapping the one or more complete formal actions to one or more executable scripts, wherein an executable script is a code that can be executed on a computing device; and
executing, by the one or more processors, the one or more executable scripts according to the sequence of the one or more complete formal actions.