US 2023/0419955 A1 · MACHINE-TRAINED NETWORK DETECTING CONTEXT-SENSITIVE WAKE EXPRESSIONS FOR A DIGITAL ASSISTANT

Abstract

Some embodiments provide a method of training a MT network to detect a wake expression that directs a digital assistant to perform an operation based on a request that follows the expression. The MT network includes processing nodes with configurable parameters. The method iteratively selects different sets of input values with known sets of output values. Each of a first group of input value sets includes a vocative use of the expression. Each of a second group of input value sets includes a non-vocative use of the expression. For each set of input values, the method uses the MT network to process the input set to produce an output value set and computes an error value that expresses an error between the produced output value set and the known output value set. Based on the error values, the method adjusts configurable parameters of the processing nodes of the MT network.

Claims21

Dependent claims

1-19. (canceled)

A non-transitory machine readable medium storing a program for execution by at least one hardware processing unit of a digital assistant of a device to direct the digital assistant of the device to perform an operation based on a vocative use of a wake expression, the program comprising sets of instructions for:

capturing an audio input comprising (i) a vocative use of a wake expression associated with the digital assistant and (ii) a set of one or more instructions for the device;
processing the audio input with a machine-trained network comprising a plurality of layers of processing nodes that are trained through machine learning to differentiate vocative uses of the wake expression from non-vocative uses of the wake expression, wherein vocative uses of the wake expression include a name associated with the digital assistant with a tonal inflection of the name that is associated with an invocation of the digital assistant;
based on an output of the machine-trained network specifying that the audio input includes the vocative use of the wake expression, directing the digital assistant of the device to perform an operation associated with the set of one or more instructions in the audio input.

Dependencies are inferred from the claim text.

Facts

Application

18/241,885Filed 3 Sep 2023

Priority

9 Apr 2018

Published

28 Dec 2023

Term ends

Not estimatedThe source records don’t support an estimate.

Classification14 codes

G10L 15/16
Speech classification or search: using artificial neural networks
G06N 3/044
Architecture, e.g. interconnection topology: recurrent networks, e.g. Hopfield networks
G06N 3/0442
Recurrent networks, e.g. Hopfield networks: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
G06N 3/049
Architecture, e.g. interconnection topology: temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs

Family

Application62/654,529Us provisional application · filed 9 Apr 2018

Application16/376,637Continuation · filed 5 Apr 2019

Application17/671,557Continuation · filed 14 Feb 2022

Application18/241,885Continuation

US 11,250,840 B1Related patent

US 11,749,263 B1Related patent

US 2023/0419955 A1This patent

From the US filing history. Foreign family members aren’t listed.

Machine-trained network detecting context-sensitive wake expressions for a digital assistant

Life of this patent

Machine-trained network detecting context-sensitive wake expressions for a digital assistant

Life of this patent

Abstract

Claims21