	US 11,941,367 B2
	Question generation by intent prediction
Jacob Lewis, New York, NY (US); Ruchi Asthana, New York, NY (US); Jennifer A. Mallette, Vienna, VA (US); and Steven Ware Jones, Astoria, NY (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on May 29, 2021, as Appl. No. 17/334,714.
Prior Publication US 2022/0382993 A1, Dec. 1, 2022
Int. Cl. G06F 40/35 (2020.01); G06F 40/30 (2020.01); G06F 40/40 (2020.01); H04L 51/02 (2022.01); G06F 40/216 (2020.01); G06F 40/279 (2020.01); G06N 3/044 (2023.01); G06N 3/08 (2023.01)

CPC G06F 40/35 (2020.01) [G06F 40/30 (2020.01); G06F 40/40 (2020.01); H04L 51/02 (2013.01); G06F 40/216 (2020.01); G06F 40/279 (2020.01); G06N 3/044 (2023.01); G06N 3/08 (2013.01)]

14 Claims

1. A computer system for question generation, the computer system comprising:

one or more computer processors;

one or more computer readable storage devices; and

stored program instructions on the one or more computer readable storage devices for execution by the one or more computer processors, the stored program instructions comprising:

program instructions to receive user utterance data;

program instructions to determine an intent confidence vector for the user utterance data;

program instructions to train the next user-intent prediction model comprising:

program instructions to receive historic chatbot conversation data, wherein the chatbot conversation data comprises labeled user-intents and intent confidence vectors for each user utterance of each conversation;

program instructions to separate the chatbot conversation data into training data and validation data;

program instructions to generate a matrix including all the intent confidence vectors for all historic conversation training data;

program instructions to train a machine learning model to classify user-intents using the matrix of training data;

program instructions to drop out at least one intent confidence vector during training;

program instructions to validate the machine learning model using the validation data; and

program instructions to provide a trained next intent predictor mode for use in generating questions:

program instructions to predict, by the trained next user-intent prediction model, a next user-intent confidence vector using the intent confidence vector;

program instructions to generate a next question set using the next user-intent confidence vector;

program instructions to adjust a confidence value of the next user-intent confidence vector according to value ranking for a predefined type of a next user intent provided by a productive value ranking algorithm;

program instructions to generate an ordered version of the next question set ordered according to next user intent confidence vector values adjusted according to the productive value ranking algorithm; and

program instructions to output a subset of the ordered version having a highest value to the user.