Intent identification for agent matching by assistant systems

Patent metadata

Field	Value
Publication number	US-11544305-B2
Application number	US-202016987043-A
Country	US
Kind code	B2
Filing date	Aug 6, 2020
Priority date	Apr 20, 2018
Publication date	Jan 3, 2023
Grant date	Jan 3, 2023

Abstract

In one embodiment, a method includes receiving a user request from a client system associated with a first user, wherein the user request is associated with a semantic-intent, identifying one or more dialog-intents associated with the user request based on the semantic-intent and context information associated with the user request, wherein each dialog-intent is a sub-intent of the semantic-intent, determining one or more agents for executing one or more tasks associated with the one or more dialog-intents, and sending instructions for presenting information returned from the one or more agents responsive to executing the one or more tasks to the client system.

First claim

What is claimed is:

1. A method comprising, by one or more computing systems:
receiving, from a client system associated with a first user, a user request, wherein the user request is associated with a semantic-intent;
identifying a plurality of dialog-intents associated with the user request based on the semantic-intent and context information associated with the user request, wherein each dialog-intent is a sub-intent of the semantic-intent;
determining a plurality of agents for executing a plurality of tasks associated with the plurality of dialog-intents; and
sending, to the client system, instructions for presenting information returned from the plurality of agents responsive to executing the plurality of tasks.

2. The method of claim 1 , further comprising:
parsing, by a natural-language understanding module, the user request to identify one or more slots, wherein identifying the plurality of dialog-intents is further based on the one or more slots.

3. The method of claim 2 , further comprising:
sending the plurality of dialog-intents and the one or more slots to the plurality of agents for executing the plurality of tasks; and
receiving, from the plurality of agents, the information responsive to executing the plurality of tasks.

4. The method of claim 2 , wherein identifying the plurality of dialog-intents associated with the user request comprises:
determining a plurality of candidate dialog-intents based on one or more domains associated with the semantic-intent and the one or more slots;
calculating a plurality of confidence scores for the plurality of candidate dialog-intents; and
identifying two or more of the candidate dialog-intents based on their respective confidence scores.

5. The method of claim 1 , wherein identifying the plurality of dialog-intents is based on a ranker model.

6. The method of claim 5 , wherein the ranker model is customized based on user profile data associated with the first user.

7. The method of claim 5 , wherein the ranker model is a machine-learning model trained based on a plurality of training samples comprising one or more of: (1) a plurality of user requests, (2) a plurality of positive dialog-intents, or (3) a plurality of negative dialog-intents.

8. The method of claim 7 , wherein the plurality of training samples are generated based on one or more dry runs, each dry run comprising:
accessing a dry-run request, wherein the dry-run request is associated with one or more domains;
executing, via a plurality of agents, a plurality of tasks associated with the dry-run request;
determining a plurality of dialog-intents based on information returned from each of the plurality of agents responsive to executing the plurality of tasks;
selecting one or more dialog-intents from the determined plurality of dialog-intents; and
annotating the selected one or more dialog-intents as positive dialog-intents and remaining non-selected dialog-intents as negative dialog-intents.

9. The method of claim 8 , further comprising generating feature representations for the plurality of training samples based on one or more of:
a plurality of capability values associated with the plurality of agents, each capability value indicating a confidence of a corresponding agent being able to execute a particular task;
information returned from the plurality of agents responsive to executing the plurality of tasks;
one or more dialog states associated with one or more dry-run requests corresponding to the one or more dry runs;
a plurality of semantic-intents associated with the one or more dry-run requests corresponding to the one or more dry runs;
one or more device contexts of one or more client systems associated with one or more users associated with the one or more dry runs; or
user profile data associated with the one or more users.

10. The method of claim 9 , wherein each dry-run request in each dry run is associated with a particular dialog session, and wherein each dialog state indicates one or more of a domain or a context associated with the particular dialog session at a time associated with the dialog session.

11. The method of claim 9 , further comprising:
generating feature representations for the information returned from the plurality of agents responsive to executing the plurality of tasks; and
re-training the ranker model based on the generated feature representations.

12. The method of claim 1 , wherein the user request is associated with one or more domains, and wherein each of the plurality of dialog-intents is associated with one of the one or more domains.

13. The method of claim 1 , wherein the presented information comprises one or more of:
a character string;
an audio clip;
an image; or
a video clip.

14. The method of claim 1 , further comprising determining one or more modalities for the presented information.

15. The method of claim 14 , wherein determining the one or more modalities for the presented information comprises:
identifying contextual information associated with the first user;
identifying contextual information associated with the client system; and
determining the one or more modalities based on the contextual information associated with the first user and the contextual information associated with the client system.

16. The method of claim 1 , wherein the plurality of agents execute the plurality of tasks in parallel.

17. The method of claim 1 , wherein each of the plurality of agents is implemented with an application programming interface (API), and wherein the API receives a command from a dialog engine for executing one of the plurality of tasks.

18. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:
receive, from a client system associated with a first user, a user request, wherein the user request is associated with a semantic-intent;
identify a plurality of dialog-intents associated with the user request based on the semantic-intent and context information associated with the user request, wherein each dialog-intent is a sub-intent of the semantic-intent;
determine a plurality of agents for executing a plurality of tasks associated with the plurality of dialog-intents; and
send, to the client system, instructions for presenting information returned from the plurality of agents responsive to executing the plurality of tasks.

19. A system comprising: one or more processors; and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to:
receive, from a client system associated with a first user, a user request, wherein the user request is associated with a semantic-intent;
identify a plurality of dialog-intents associated with the user request based on the semantic-intent and context information associated with the user request, wherein each dialog-intent is a sub-intent of the semantic-intent;
determine a plurality of agents for executing a plurality of tasks associated with the plurality of dialog-intents; and
send, to the client system, instructions for presenting information returned from the plurality of agents responsive to executing the plurality of tasks.

Assignees

• Meta Platforms Inc

Inventors

• Liu Baiyang
• Dumoulin Benoit F
• Garcia Jurado Suarez Carlos
• Liu Xiaohu

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06F3/011

  Arrangements for interaction with the human body, e.g. for user immersion in virtual reality (blind teaching G09B21/00) · CPC title

• G06F18/2411

  based on the proximity to a decision surface, e.g. support vector machines · CPC title

• G06F3/017

  Gesture based interaction, e.g. based on a set of recognized hand gestures (interaction based on gestures traced on a digitiser G06F3/04883) · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title