METHODS AND SYSTEMS FOR PROVIDING PERSONALIZED AND CONTEXT-AWARE SUGGESTIONS

• US 20140108307A1
• Filed: 01/23/2013
• Published: 04/17/2014
• Est. Priority Date: 10/12/2012
• Status: Abandoned Application

• Current Assignee: Wipro Limited
• Original Assignee: Wipro Limited
• Inventors: RAGHUNATHAN, Sriraman Kandhadai, Jaswal, Abhijeet
• Application Number:
  US13/747,509
• Publication Number:
  US 20140108307A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

Embodiments of the disclosure relate to methods and systems for providing personalized and context-aware suggestions to a user. The method includes providing a user profile. Further, the method includes establishing contextual information regarding the user. Thereafter, one or more suggestions are provided to the user based on the user profile and the contextual information. Subsequently, the user profile based on the user feedback in response to the suggestion is modified. The user profile may be modified using a machine learning algorithm executed on a processor in order to improve the quality of the personalized and context-aware suggestions. In certain embodiments, the personalized and context-aware suggestions can be provided while the user is in a vehicle or while the user is operating a vehicle.

Claims

1. A method of providing personalized and context-aware suggestions to a user in a vehicle, the method comprising:

providing a user profile;

establishing contextual information relevant to the user, wherein the contextual information comprises information obtained from social communications of the user;

providing a suggestion to the user, while the user is in a vehicle, based on the user profile and the contextual information; and

modifying, using a machine learning algorithm executed on a processor, the user profile based on user feedback received in response to the suggestion.

2. The method of claim 1, wherein providing the user profile comprises modifying a pre-defined user template with information about the user.

3. The method of claim 1, wherein establishing the contextual information comprises obtaining the contextual information from at least one of:

on-board vehicle devices;

third-party content and services;

or

email accounts, social networks, calendars, and contacts of the user.

4. The method of claim 1, wherein the user profile includes information regarding at least one of:

the user'"'"'s demographic background;

previous destinations or navigation routes of the user;

the user'"'"'s contacts;

the user'"'"'s schedule;

the user'"'"'s preferences or tastes;

or

activities conducted by the user, including at least one of visiting a particular address or other location, shopping, eating food at a restaurant, listening to music, or watching videos.

5. The method of claim 1, wherein the social communications of the user include at least one of:

emailing a contact;

chatting with a contact;

messaging a contact;

calling a contact;

or

posting a message on a social media platform.

6. The method of claim 1, wherein the contextual information further comprises at least one of:

a route provided by the user;

a present date and time;

a measurement of traffic conditions being experienced by the user;

the proximity of contacts known to the user;

the make, model, and/or other identifying characteristics of a vehicle occupied by the user;

geographic points of interest;

or

weather conditions.

7. The method of claim 1, wherein the machine learning algorithm modifies the user profile based on user feedback received in response to the suggestion by:

categorizing the user feedback as positive feedback or negative feedback; and

adjusting priorities associated with elements of the user profile based on whether positive feedback or negative feedback was received in response to the suggestion.

8. The method of claim 1, wherein providing the suggestion to the user comprises providing the suggestion through a voice based human machine interface.

9. The method of claim 8, wherein the user feedback is received through the voice based human machine interface and processed using a natural language processing algorithm to modify the user profile.

10. A system for providing personalized and context-aware suggestions to a user, the system comprising:

one or more hardware processors; and

a memory storing instructions to configure the one or more hardware processors,

wherein the one of more hardware processors are configured by the instructions to;

provide a user profile;

establish contextual information relevant to the user, wherein the contextual information comprises information obtained from social communications of the user;

provide a suggestion to the user, while the user is in a vehicle, based on the user profile and the contextual information; and

modify the user profile based on user feedback received in response to the suggestion using a machine learning algorithm being executed on the one or more hardware processors.

11. The system of claim 10, wherein the one or more hardware processors are further configured by the instructions to provide the user profile by modifying a pre-defined user template with information about the user.

12. The system of claim 10, wherein the one or more hardware processors are further configured to establish the contextual information by obtaining the contextual information from at least one of:

on-board vehicle devices;

third-party content and services;

or

user email accounts, social networks, calendars, and contacts.

13. The system of claim 10, wherein the user profile includes information regarding at least one of:

the user'"'"'s demographic information, office address, residence address, family information, and/or personal relationships;

previous destinations or navigation routes provided by the user;

the user'"'"'s contacts;

the user'"'"'s schedule;

the user'"'"'s preferences or tastes;

or

activities conducted by the user, including at least one of visiting a particular address or other location, shopping, eating food at a restaurant, listening to music, and watching videos.

14. The system of claim 10, wherein the contextual information further comprises information regarding at least one of:

a route provided user;

a present date and time;

a measurement of traffic conditions being experienced by the user;

the proximity of contacts known to the user;

the make, model, and/or other identifying characteristics of a vehicle occupied by the user;

geographic points of interest;

or

weather conditions.

15. The system of claim 10, wherein the social communications of the user include at least one of:

emailing a contact;

chatting with a contact;

messaging a contact;

calling a contact;

or

posting a message on a social media platform.

16. The system of claim 10, wherein the machine learning algorithm modifies the user profile based on user feedback received in response to the suggestion by:

categorizing the user feedback as positive feedback or negative feedback; and

adjusting priorities associated with elements of the user profile based on whether positive feedback or negative feedback was received in response to the suggestion.

17. The system of claim 10, wherein the one or more hardware processors are further configured by the instructions to provide the suggestion through a voice based human machine interface.

18. The system of claim 17, wherein the one or more hardware processors are further configured by the instructions to receive the user feedback through the voice based human machine interface and process the user feedback using a natural language processing algorithm to modify the user profile.

19. A non-transitory computer-readable medium storing instructions for providing personalized and context-aware suggestions to a user in a vehicle, wherein execution of the instructions by one or more processors causes the one or more processors to:

provide a user profile;

establish contextual information relevant to the user, wherein the contextual information comprises information obtained from social communications of the user;

provide a suggestion to the user, while the user is in a vehicle, based on the user profile and the contextual information; and

modify the user profile based on user feedback received in response to the suggestion using a machine learning algorithm.

20. The non-transitory computer readable medium of claim 19, wherein the stored instructions further cause the one or more processors to provide the suggestion through a voice based human machine interface.