CPC G06N 3/088 (2013.01) [G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 5/01 (2023.01)] | 20 Claims |
1. A computer-implemented method comprising:
generating, using a training dataset, a decision tree, wherein each node of the decision tree represents a question sequence, wherein each question sequence comprises a plurality of questions intended to elicit a response usable in recommending a first configuration of a plurality of computing resources, wherein the training dataset comprises a plurality of asset characteristics of assets within previously configured computing environments;
receiving a user response to a first question sequence, wherein the first question sequence is represented by a first node of the decision tree;
generating, by inputting the user response to a recursive neural network (RNN), a second question sequence and a first deviation value, the first deviation value indicative of a deviation between the second question sequence and a plurality of decision tree question sequences, each decision tree question sequence in the plurality of decision tree question sequences represented by a next node in the decision tree, each next node in the decision tree connected by an edge to the first node;
disambiguating, using a disambiguation rule, the user response, the disambiguating performed responsive to determining that the first deviation value exceeds a threshold, the disambiguating resulting in a disambiguated user response;
generating, by inputting the user response to the RNN, a third question sequence and a second deviation value, the second deviation value indicative of a deviation between the third question sequence and the plurality of decision tree question sequences;
generating, using a generator portion of a generative associative network (GAN), a custom question sequence;
generating, using a user response to the custom question sequence, a projected usage parameter; and
generating, using the projected usage parameter, the first configuration of the plurality of computing resources.
|