CRAFTING FEEDBACK DIALOGUE WITH A DIGITAL ASSISTANT

• US 20160342317A1
• Filed: 05/20/2015
• Published: 11/24/2016
• Est. Priority Date: 05/20/2015
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Inventors: LIM, Melissa Nicole, MITCHELL, Margaret Ann, QUIRK, Christopher Brian
• Granted Patent: US 10,446,142 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G10L 15/1822 Parsing for meaning understanding
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 2015/0635 updating or merging of old and new templates; Mean values; Weighting
• CPC Class Codes: G10L 2015/0638 Interactive procedures

Abstract

Examples described herein dynamically personalize a digital assistant for a specific user, creating a personal connection between the digital assistant and the user. The digital assistant accesses user activity and generates queries based on the user activity. The digital assistant facilitates natural language conversations as machine learning sessions between the digital assistant and the user using the one or more queries to learn the user'"'"'s preferences and receives user input from the user during the learning session in response to the queries. The digital assistant dynamically updates a personalized profile for the user based on the user input during the natural language conversations.

Claims

1. A system for personalizing a digital assistant, the system comprising:

a processing unit;

a memory communicatively coupled to the processing unit, the memory maintaining activity logs corresponding to one or more components of one or more user devices; and

a digital assistant, executed by the processing unit, the digital assistant further executed to;

process the activity logs maintained in the memory;

generate one or more questions corresponding to the processed activity logs;

conduct a natural language conversation as a learning session based on the one or more questions;

receive user input during the learning session based on the one or more questions;

identify sentiment associated with at least one facet of the user input; and

update a user profile based on the user input and the identified sentiment of the user input.

2. The system of claim 1, wherein the one or more components include at least one of a global positioning system, an Internet browser, a sentiment classifier, an electronic messaging client, a text messaging client, a camera, an error log, or a personalized knowledge base.

3. The system of claim 1 wherein the digital assistant is further executed to:

maintain the one or more questions in a learning queue;

determine whether a number of stored questions in the learning queue has reached a threshold; and

initiate the learning session responsive to determining that the threshold has been reached.

4. The system of claim 1 wherein the digital assistant is further executed to:

generate one or more inferences related to user preferences based on the activity logs; and

generate at least one question directed towards confirming at least one of the one or more generated inferences.

5. The system of claim 1 wherein the digital assistant is further executed to:

generate a response during the learning session based at least in part on the identified sentiment.

6. The system of claim 1 wherein the digital assistant is further executed to:

label the at least one facet identified in the user input according to the identified sentiment; and

update the user profile with the labeled facet.

7. A mobile device comprising:

a processing unit;

a memory communicatively coupled to the processing unit; and

a digital assistant executed by the processing unit to;

process activity logs maintained on the memory;

identify one or more areas of desired feedback based on the activity logs;

generate one or more questions corresponding to the one or more areas of desired feedback;

initiate a natural language conversation based on the one or more questions;

receive user input during the natural language conversation based on the one or more questions; and

perform at least one task related to a user preference based on the user input.

8. The mobile device of claim 7 wherein the digital assistant is further executed to:

generate one or more inferences related to user preferences based on the activity logs; and

generate at least one question directed towards a task associated with the at least one inference.

9. The mobile device of claim 7 further comprising:

a global positioning system generating one or more signals corresponding to at least one location associated with at least one user device, the one or more signals stored in the activity logs; and

the digital assistant further executed to process the one or more signals to identify the one or more areas of desired feedback.

10. The mobile device of claim 7 further comprising:

a browser executed by the processing unit, the browser generating one or more signals corresponding to browser history associated with at least one user device, the one or more signals stored in the activity logs; and

the digital assistant further executed to process the one or more signals to identify the one or more areas of desired feedback.

11. The mobile device of claim 7 further comprising:

an electronic messaging client executed by the processing unit, the electronic messaging client generating one or more signals corresponding to text input associated with at least one user device, the one or more signals stored in the activity logs; and

the digital assistant further executed to process the one or more signals to identify the one or more areas of desired feedback.

12. The mobile device of claim 7 further comprising:

a camera generating one or more signals corresponding to at least one captured image, the one or more signals stored in the activity logs; and

the digital assistant further executed to process the one or more signals to identify the one or more areas of desired feedback.

13. The mobile device of claim 7 further comprising:

an error log generating one or more signals corresponding to at least one user-initiated task failure, the one or more signals stored in the activity logs; and

the digital assistant further executed to process the one or more signals to identify the one or more areas of desired feedback.

14. A method for digital assistant personalization, the method comprising:

accessing, by a digital assistant implemented on a computing device, activity logs;

generating one or more queries based on the accessed activity logs;

initiating a natural language conversation as a machine learning session between the digital assistant and a user using the one or more queries;

receiving user input during the natural language conversation in response to the one or more queries; and

updating a personalized profile associated with the user based on the user input to personalize the digital assistant.

15. The method of claim 14 wherein initiating the machine learning session further comprises:

maintaining the one or more queries in a learning queue;

determining whether a number of stored queries in the learning queue has reached a threshold; and

initiating the natural language conversation responsive to determining that the threshold has been reached.

16. The method of claim 14 further comprising:

performing sentiment analysis on the user input during the natural language conversation;

identifying sentiment directed towards a facet of at least one of the one or more queries; and

generating a response to the user input during the natural language conversation of the natural language conversation using the identified sentiment.

17. The method of claim 14 wherein generating the one or more queries further comprises:

processing the activity logs, including performing keyword recognition on at least one of browser history data, text messaging data, electronic messaging data, or social media application data.

18. The method of claim 14 wherein generating the one or more queries further comprises:

processing the activity logs, including identifying one or more locations corresponding to a computing device location history using global positioning system data associated with the computing device and a search engine.

19. The method of claim 14 wherein generating the one or more queries further comprises:

processing the activity logs, including detecting one or more images captured by a camera associated with the computing device and identifying at least one entity in at least one of the one or more images.

20. The method of claim 14 wherein generating the one or more queries further comprises:

processing the activity logs, including identifying at least one failed user-initiated task.