US 12,423,620 B2
Intent-based task representation learning using weak supervision
Oriana Riva, Redmond, WA (US); Michael Gamon, Seattle, WA (US); Sujay Kumar Jauhar, Kirkland, WA (US); Mei Yang, Redmond, WA (US); Sri Raghu Malireddi, Vancouver (CA); Timothy C. Franklin, Seattle, WA (US); and Naoki Otani, Pittsburgh, PA (US)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Mar. 31, 2022, as Appl. No. 17/710,880.
Claims priority of provisional application 63/305,191, filed on Jan. 31, 2022.
Prior Publication US 2023/0244989 A1, Aug. 3, 2023
Int. Cl. G06N 3/04 (2023.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01)
CPC G06N 20/00 (2019.01) [G06F 40/30 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
a processor; and
memory, including machine-readable instructions, that when executed by the processor, cause the processor to:
receive task data representing a task;
encode at least a portion of the task data using an encoder;
generate a task embedding from an output generated by the encoder and a type embedding indicating a type of task associated with the task data, the task embedding including intent information indicating a predicted intent associated with the task data, wherein the task embedding is generated using a machine learning model trained on external semantically rich data sets that include contextual information describing tasks different than the task data representing the task, and the machine learning model is optimized over a plurality of auxiliary tasks; and
provide the generated task embedding to a receiving application, the generated task embedding including additional task-related data that is different than the task data representing the task.