US 12,014,146 B2
Techniques for out-of-domain (OOD) detection
Thanh Long Duong, Seabrook (AU); Mark Edward Johnson, Castle Cove (AU); Vishal Vishnoi, Redwood City, CA (US); Crystal C. Pan, Palo Alto, CA (US); Vladislav Blinov, Melbourne (AU); Cong Duy Vu Hoang, Wantima South (AU); Elias Luqman Jalaluddin, Seattle, WA (US); Duy Vu, Melbourne (AU); and Balakota Srinivas Vinnakota, Sunnyvale, CA (US)
Assigned to Oracle International Corporation, Redwood Shores, CA (US)
Filed by Oracle International Corporation, Redwood Shores, CA (US)
Filed on Aug. 2, 2023, as Appl. No. 18/364,298.
Application 18/364,298 is a continuation of application No. 17/217,909, filed on Mar. 30, 2021, granted, now 11,763,092.
Claims priority of provisional application 63/002,139, filed on Mar. 30, 2020.
Prior Publication US 2023/0376696 A1, Nov. 23, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 40/30 (2020.01); G06F 40/205 (2020.01); G06F 40/289 (2020.01); G06N 20/00 (2019.01); H04L 51/02 (2022.01)
CPC G06F 40/30 (2020.01) [G06F 40/289 (2020.01); G06N 20/00 (2019.01); H04L 51/02 (2013.01); G06F 40/205 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
accessing an utterance and a target domain of a chatbot;
generating a sentence embedding for the utterance;
predicting a first probability as to whether the utterance belongs to the target domain of the chatbot based on the sentence embedding for the utterance and a distance or density deviation between the sentence embedding and an embedding representation for a cluster of a plurality of clusters of in-domain utterances associated with the target domain of the chatbot;
predicting a second probability as to whether the utterance belongs to the target domain of the chatbot based on the sentence embedding for the utterance and a similarity or difference between the sentence embedding and an embedding representation for a cluster of the plurality of clusters of in-domain utterances associated with the target domain of the chatbot;
determining, based on the first probability and the second probability, a final probability as to whether the utterance belongs to the target domain of the chatbot; and
classifying the utterance as in-domain or out-of-domain for the chatbot based on the final probability.