Smart replies using an on-device model

Patent metadata

Field	Value
Publication number	US-10846618-B2
Application number	US-201715686954-A
Country	US
Kind code	B2
Filing date	Aug 25, 2017
Priority date	Sep 23, 2016
Publication date	Nov 24, 2020
Grant date	Nov 24, 2020

Abstract

A computing device may receive a communication sent from an external computing device. At least one processor of the computing device may determine, using an on-device machine-trained model and based at least in part on the communication, one or more candidate responses to the communication. The at least one processor may receive an indication of a user input that selects a candidate response from the one or more candidate responses. Responsive to receiving the indication of the user input that selects the candidate response, the at least one processor may send the candidate response to the external computing device.

First claim

What is claimed is:

1. A method comprising:
receiving, by at least one processor of a computing device, a communication sent from an external computing device;
determining, by the at least one processor using an on-device machine-trained model, and based at least in part on the communication, one or more candidate responses to the communication, wherein the on-device machine-trained model comprises indications of a plurality of projected nodes, wherein each of the plurality of projected nodes are associated with a ranked list of predicted responses, and wherein determining the one or more candidate responses to the communication further comprises:
projecting, by the at least one processor using a random projection function, the received communication into a hash signature;
determining, by the at least one processor, a projected node that is associated with the hash signature from the plurality of projected nodes; and
determining, by the at least one processor, the one or more candidate responses to the communication from the ranked list of predicted responses;

receiving, by the at least one processor, an indication of a user input that selects a candidate response from the one or more candidate responses; and
responsive to receiving the indication of the user input that selects the candidate response, sending, by the at least one processor, the candidate response to the external computing device.

2. The method of claim 1 , wherein the on-device machine trained model is trained via semi-supervised machine learning at an external computing system to associate the plurality of projected nodes with respective ranked lists of predicted responses.

3. The method of claim 1 , further comprising:
determining, by the at least one processor, one or more personalized candidate responses based at least in part on a communication history of a user of the computing device, wherein the user is an intended recipient of the communication; and
including, by the at least one processor, the one or more personalized candidate responses in the one or more candidate responses.

4. The method of claim 3 , wherein determining the one or more personalized candidate responses further comprises:
determining, by the at least one processor using the on-device machine-trained model, and based at least in part on the communication, a ranked list of predicted responses to the communication, wherein the one or more candidate responses are selected from the ranked list of responses;
in response to determining that the communication history of the user includes a previous response sent by the user that belongs to a same semantic cluster as a predicted response, including, by the at least one processor, the previous response sent by the user in the one or more personalized candidate responses.

5. The method of claim 4 , wherein including the previous response sent by the user in the one or more personalized candidate responses is further responsive to determining that the previous response matches one of a plurality of predicted responses in a response space of the on-device machine-trained model that belongs to the semantic cluster.

6. A computing device comprising:
a computer-readable storage medium configured to store an on-device machine-trained model;
at least one processor operably coupled to the computer-readable storage medium and configured to:
receive a communication sent from an external computing device;
determine, using the on-device machine-trained model and based at least in part on the communication, one or more candidate responses to the communication, wherein the on-device machine-trained model comprises indications of a plurality of projected nodes, wherein each of the plurality of projected nodes are associated with a ranked list of predicted responses, and wherein the processor is configured to determine the one or more candidate responses to the communication by at least being configured to:
project, using a random projection function, the received communication into a hash signature;
determine a projected node that is associated with the hash signature from the plurality of projected nodes; and
determine the one or more candidate responses to the communication from the ranked list of predicted responses;

receive an indication of a user input that selects a candidate response from the one or more candidate responses; and
responsive to receiving the indication of the user input that selects the candidate response, send the candidate response to the external computing device.

7. The computing device of claim 6 , wherein the on-device machine trained model is trained via semi-supervised machine learning at an external computing system to associate the plurality of projected nodes with respective ranked lists of predicted responses.

8. The computing device of claim 6 , wherein the at least one processor is further configured to:
determine one or more personalized candidate responses based at least in part on a communication history of a user of the computing device, wherein the user is an intended recipient of the communication; and
include the one or more personalized candidate responses in the one or more candidate responses.

9. The computing device of claim 8 , wherein the at least one processor is further configured to:
determine, using the on-device machine-trained model and based at least in part on the communication, a ranked list of predicted responses to the communication, wherein the one or more candidate responses are selected from the ranked list of responses; and
in response to determining that the communication history of the user includes a previous response sent by the user that belongs to a same semantic cluster as a predicted response, include the previous response sent by the user in the one or more personalized candidate responses.

10. The computing device of claim 9 , wherein the at least one processor is further configured to:
include the previous response sent by the user in the one or more personalized candidate responses further in response to determining that the previous response matches one of a plurality of predicted responses in a response space of the on-device machine-trained model that belongs to the semantic cluster.

11. A computer-readable storage medium encoded with instructions that, when executed, cause at least one processor of a computing device to:
receive a communication sent from an external computing device;
determine, using an on-device machine-trained model and based at least in part on the communication, one or more candidate responses to the communication, wherein the on-device machine-trained model comprises indications of a plurality of projected nodes, wherein each of the plurality of projected nodes are associated with a ranked list of predicted responses, and wherein the instructions cause the processor to determine the one or more candidate responses to the communication by at least causing the processor to:
project, using a random projection function, the received communication into a hash signature;
determine a projected node that is associated with the hash signature from the plurality of projected nodes; and
determine the one or more candidate responses to the communication from the ranked list of predicted responses;

receive an indication of a user input that selects a candidate response from the one or more candidate responses; and
responsive to receiving the indication of the user input that selects the candidate response, send the candidate response to the external computing device.

12. The computer-readable storage medium of claim 11 , wherein the on-device machine trained model is trained via

Assignees

• Google Inc
• Google Llc

Inventors

• Ravi Sujith
• Rudick Thomas Matthew
• Beach Nathan Dickerson
• Mcgregor Jr John Patrick
• Ranieri Mirko

Classifications

• G06N20/00 Primary

  Machine learning · CPC title

• G06N5/04

  Inference or reasoning models · CPC title

• G06Q10/107 Primary

  Computer-aided management of electronic mailing [e-mailing] · CPC title

• G06Q10/10

  Office automation; Time management · CPC title

• H04L51/02

  using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages · CPC title