US 11,929,070 B1
Machine learning label generation
Ruhi Sarikaya, Redmond, WA (US); Zheng Du, Bellevue, WA (US); Xiaohu Liu, Bellevue, WA (US); Kai Liu, Redmond, WA (US); Sriharsha Venkata Chintalapati, Hyderabad (IN); Chenlei Guo, Redmond, WA (US); Hung Tuan Pham, Kirkland, WA (US); Joe Pemberton, Seattle, WA (US); Zhenyu Yao, Sammamish, WA (US); and Bigyan Rajbhandari, Kirkland, WA (US)
Assigned to Amazon Technologies, Inc., Seattle, WA (US)
Filed by Amazon Technologies, Inc., Seattle, WA (US)
Filed on Aug. 30, 2021, as Appl. No. 17/461,124.
Int. Cl. G10L 15/22 (2006.01); G06N 20/20 (2019.01); G10L 15/02 (2006.01); G10L 15/06 (2013.01)
CPC G10L 15/22 (2013.01) [G06N 20/20 (2019.01); G10L 15/02 (2013.01); G10L 15/063 (2013.01); G10L 2015/225 (2013.01)] 20 Claims
OG exemplary drawing
 
13. A computing system comprising:
at least one processor; and
at least one memory comprising instructions that, when executed by the at least one processor, cause the computing system to:
receive first data requesting labeled data for updating a first machine learning (ML) model, the first data including a prompt for requesting user feedback relating to processing of the first ML model;
cause a device to output the prompt to request first user feedback with respect to processing of a user input by first ML model;
receive, from the device, first user feedback data corresponding to the first user feedback;
determine, based at least in part on the first user feedback data, that processing of the user input by the first ML model resulted in incorrect output data being presented in response to the user input; and
generate, using a second ML model, first labeled data usable to update the first ML model, the second ML model generating the first labeled data based at least in part on the first user feedback data.