US 11,862,149 B2
Learning how to rewrite user-specific input for natural language understanding
Bigyan Rajbhandari, Kirkland, WA (US); Praveen Kumar Bodigutla, Cambridge, MA (US); Zhenxiang Zhou, Seattle, WA (US); Karen Catelyn Stabile, Seattle, WA (US); Chenlei Guo, Redmond, WA (US); Abhinav Sethy, Seattle, WA (US); Alireza Roshan Ghias, Seattle, WA (US); Pragaash Ponnusamy, Seattle, WA (US); and Kevin Quinn, Bellevue, WA (US)
Assigned to Amazon Technologies, Inc., Seattle, WA (US)
Filed by Amazon Technologies, Inc., Seattle, WA (US)
Filed on Sep. 2, 2021, as Appl. No. 17/464,755.
Application 17/464,755 is a continuation of application No. 16/138,447, filed on Sep. 21, 2018, granted, now 11,151,986.
Prior Publication US 2022/0059086 A1, Feb. 24, 2022
Int. Cl. G10L 15/00 (2013.01); G10L 15/18 (2013.01); G10L 15/30 (2013.01); G10L 15/22 (2006.01)
CPC G10L 15/1815 (2013.01) [G10L 15/22 (2013.01); G10L 15/30 (2013.01); G10L 2015/223 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A computer-implemented method, comprising:
receiving first input data representing a first natural language input;
using a natural language understanding (NLU) component, performing first language processing on the first input data to determine first NLU results data indicating at least an intent of the first natural language input;
using the first NLU results data, determining first output data responsive to the first natural language input;
causing presentation of the first output data;
receiving second input data;
processing the second input data to determine the second input data indicates negative feedback corresponding to the first output data;
based on the second input data indicating the negative feedback, retraining the NLU component to determine an updated NLU component;
after determining the updated NLU component, receiving third input data representing the first natural language input;
using the updated NLU component, performing second language processing on the third input data to determine second NLU results data indicating at least an intent of the first natural language input as represented in the third input data, wherein the first NLU results data is different from the second NLU results data; and
using the second NLU results data, determining second output data responsive to the first natural language input as represented in the third input data, wherein the second output data is different from the first output data.