US 12,214,661 B2
Question-and-answer system and method of controlling the same
Cheoneum Park, Gyeonggi-do (KR); Mirye Lee, Seoul (KR); Donghyeon Kim, Seoul (KR); Cheongjae Lee, Gyeonggi-do (KR); and Sung Wook Kim, Gyeonggi-do (KR)
Assigned to Hyundai Motor Company, Seoul (KR); and Kia Corporation, Seoul (KR)
Filed by Hyundai Motor Company, Seoul (KR); and Kia Corporation, Seoul (KR)
Filed on Feb. 16, 2022, as Appl. No. 17/673,287.
Claims priority of application No. 10-2021-0021874 (KR), filed on Feb. 18, 2021.
Prior Publication US 2022/0258607 A1, Aug. 18, 2022
Int. Cl. B60K 35/00 (2024.01); B60K 35/10 (2024.01); B60K 35/28 (2024.01); G06N 3/04 (2023.01); G06N 5/04 (2023.01)
CPC B60K 35/00 (2013.01) [G06N 3/04 (2013.01); G06N 5/04 (2013.01); B60K 35/10 (2024.01); B60K 35/28 (2024.01); B60K 2360/148 (2024.01); B60K 2360/161 (2024.01)] 10 Claims
OG exemplary drawing
 
1. A question-and-answer system comprising:
a memory in which a plurality of representative questions are stored to match a plurality of answers corresponding respectively to the plurality of representative questions;
a learning module configured to output a representative question corresponding to an input sentence from among the stored plurality of representative questions; and
an output module configured to search the memory for an answer that matches the output representative question and output the searched answer;
wherein the learning module is configured to perform multi task learning by using a plurality of extended sentences for the plurality of representative questions as input data, and by using the plurality of representative questions corresponding respectively to the plurality of extended sentences, and a plurality of categories to which the plurality of extended sentences belong, respectively, as output data;
wherein the learning module is further configured to:
perform the multi task learning by using the plurality of extended sentences as input data, and by using the plurality of representative questions, the plurality of categories, and a plurality of named entities included in the plurality of extended sentences, respectively, as output data;
in the multi task learning, classify a representative question corresponding to the input data from among the stored plurality of representative questions, a category to which the input data belongs from among the plurality of categories, and a named entity included in the input data from among the plurality of named entities;
calculate a loss value of the classified representative question, a loss value of the classified category, and a loss value of the classified named entity; and
adjust a weight of a deep learning model used for the multi-tasking learning based on the three calculated loss values.