Artificial Intelligence System for Inferring Grounded Intent

Patent metadata

Field	Value
Publication number	US-2019251417-A1
Application number	US-201815894913-A
Country	US
Kind code	A1
Filing date	Feb 12, 2018
Priority date	Feb 12, 2018
Publication date	Aug 15, 2019
Grant date	—

Abstract

Techniques for enabling an artificial intelligence system to infer grounded intent from user input, and automatically suggest and/or execute actions associated with the predicted intent. In an aspect, core task descriptions are extracted from actionable statements identified as containing grounded intent. A machine classifier receives the core task description, actionable statements, and user input to predict an intent class for the user input. The machine classifier may be trained using unsupervised learning techniques based on weakly labeled clusters of the core task description extracted over a training corpus. The core task description may include verb-object pairs.

First claim

1 . A method for causing a computing device to digitally execute actions responsive to user input, the method comprising:
identifying an actionable statement from the user input; extracting a core task description from the actionable statement, the core task description comprising a verb entity and an object entity; assigning an intent class to the actionable statement by supplying features to a machine classifier, the features comprising the actionable statement and the core task description; and executing on the computing device at least one action associated with the assigned intent class.

2 . The method of claim 1 , further comprising:
displaying the at least one action associated with the assigned intent class to the user; and receiving user approval prior to executing the at least one action.

3 . The method of claim 1 , wherein the verb entity comprises at least one symbol from the actionable statement representing a task action, and the object entity comprises at least one symbol from the actionable statement representing an object to which the task action is applied.

4 . The method of claim 1 , the identifying the actionable statement comprising applying a commitments classifier or a request classifier to the user input.

5 . The method of claim 1 , the at least one action comprising launching an agent application on the computing device.

6 . The method of claim 1 , the features further comprising contextual features independent of the user input, the contextual features derived from prior usage of the device by a user or from parameters associated with a user profile or a cohort model.

7 . The method of claim 1 , further comprising training the machine classifier using weak supervision, the training comprising:
identifying a training statement from each of a plurality of corpus items; extracting a training description from each of the training statements; grouping the training descriptions by textual similarity into a plurality of clusters; receiving an annotation of intent associated with each of the plurality of clusters; and training the machine classifier to map each identified training statement to the corresponding annotated intent.

8 . The method of claim 7 , wherein the verb entity comprises a symbol from the corresponding training statement representing a task action, and the object entity comprises a symbol from the corresponding actionable statement representing an object to which the task action is applied. the grouping the training descriptions comprising:
grouping the training descriptions into a first set of clusters based on textual similarity of the corresponding object entities; and refining the first set of clusters into a second set of clusters based on textual similarity of the corresponding verb entities.

9 . The method of claim 7 , further comprising:
receiving user feedback indicating rejection of the at least one action associated with the assigned intent class; and training the machine classifier to map the actionable statement away from the assigned intent class.

10 . The method of claim 7 , further comprising:
receiving user feedback indicating acceptance of the at least one action associated with the assigned intent class; and training the machine classifier to reinforce mapping further instances of the actionable statement to the assigned intent class.

11 . The method of claim 7 , further comprising:
receiving user feedback comprising at least one of subjective impression by the user of the quality or utility of the assigned intent class; and training the machine classifier to map the actionable statement according to the received user feedback.

12 . The method of claim 7 , further comprising:
receiving user feedback comprising executing an alternative action distinct from the at least one action associated with the assigned intent class; and associating the alternative action with the assigned intent class.

13 . An apparatus for digitally executing actions responsive to user input, the apparatus comprising:
an identifier module configured to identify an actionable statement from the user input; an extraction module configured to extract a core task description from the actionable statement, the core task description comprising a verb entity and an object entity; and a machine classifier configured to assign an intent class to the actionable statement based on features comprising the actionable statement and the core task description; the apparatus configured to execute at least one action associated with the assigned intent class.

14 . The apparatus of claim 13 , further configured to launch an agent application to execute the at least one action.

15 . The apparatus of claim 13 , further comprising a training module for training the machine classifier using weak supervision, the training module comprising:
a training identifier configured to identify a training statement from each of a plurality of corpus items; a training extractor configured to extract a training description from each of the training statements; a clustering module configured to group the training descriptions by textual similarity into a plurality of clusters; and a manual adjustment module configured to receive an annotation of intent associated with each of the plurality of clusters; the training module further configured to train the machine classifier to map each identified training statement to the corresponding annotated intent.

16 . The apparatus of claim 15 , wherein the verb entity comprises a symbol from the corresponding training statement representing a task action, and the object entity comprises a symbol from the corresponding actionable statement representing an object to which the task action is applied. the clustering module configured to group the training descriptions by:
grouping the training descriptions into a first set of clusters based on textual similarity of the corresponding object entities; and refining the first set of clusters into a second set of clusters based on textual similarity of the corresponding verb entities.

17 . The apparatus of claim 15 , further comprising a feedback module configured to receive user feedback indicating rejection of the at least one action associated with the assigned intent class, the training module further configured to train the machine classifier to map the actionable statement away from the assigned intent class.

18 . An apparatus comprising a processor and a memory storing instructions executable by the processor to cause the processor to:
identify an actionable statement from the user input; extract a core task description from the actionable statement, the core task description comprising a verb entity and an object entity; assign an intent class to the actionable statement by supplying features to a machine classifier, the features comprising the actionable statement and the core task description; and execute using the processor at least one action associated with the assigned intent class.

19 . The apparatus of claim 18 , the memory further storing instructions to cause the processor to:
display the at least one action associated with the assigned intent class to the user; and receive user approval prior to executing the at least one action.

20 . The apparatus of claim 18 , wherein the verb entity comprises at least one symbol from the actionable statement representing a task action, and the object entity comprises at least one symbol from the actionable s

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Bennett Paul N
• Hasegawa Marcello Mendes
• Ghotbi Nikrouz
• White Ryen William
• Jha Abhishek

Classifications

• G06F40/00

  Handling natural language data (speech analysis or synthesis, speech recognition G10L) · CPC title

• G06F40/30

  Semantic analysis · CPC title

• G06F40/274

  Converting codes to words; Guess-ahead of partial word inputs · CPC title

• G06N5/022

  Knowledge engineering; Knowledge acquisition · CPC title

• G06N3/006 Primary

  based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] · CPC title