TECHNIQUES TO CAPTURE CONTEXT AND LOCATION INFORMATION AND UTILIZE HEURISTICS TO TURN LOCATION TRACKED OVER TIME AND CONTEXT INFORMATION INTO SEMANTIC LOCATION INFORMATION

• US 20120246000A1
• Filed: 12/15/2009
• Published: 09/27/2012
• Est. Priority Date: 12/15/2009
• Status: Abandoned Application

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Yarvis, Mark D., Wouhaybi, Rita H., Muse, Philip, Durham, Lenitra M., Balasundaram, Sai P., Sharma, Sangita, Wan, Chieh-Yih
• Application Number:
  US13/130,203
• Publication Number:
  US 20120246000A1
• Time in Patent Office: Days
• US Class Current: 705/14.53
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal

Abstract

An embodiment of the present invention provides a method, comprising, capturing context information of a user and using heuristics based on a common knowledge database to turn location tracked over time combined with the context information into semantic location information.

Claims

1. A method, comprising:

capturing context information of a user and using heuristics based on a common knowledge database to turn location tracked over time combined with said context information into semantic location information.

2. The method of claim 1, further comprising creating and identifying said heuristics.

3. The method of claim 2, wherein trace data of GPS coordinates are obtained continuously throughout said user'"'"'s day and first clustered to identify interesting locations, then up-leveled to a street address or business name and then semantically interpreted in one of several categories.

4. The method of claim 3, wherein said heuristics are used to semantically interpret said clustered locations upleveled to a street address or business name, into one of several categories.

5. The method of claim 4, further comprising further refining categories into a set of daily patterns.

6. The method of claim 1, further comprising distributing said semantic location information to a service provider, wherein said service provider provides an incentive to said user for said context information.

7. A method of determining when a user is in-market for a specific product and knowing the general shopping preferences and habits of users, comprising:

using web browsing behavior to determine the products said user is interested in purchasing and how said user typically likes to shop; and

wherein said web browsing behavior is determined by watching some or all web pages loaded and analyzing at least a URL, page text, and cookies associated with each loaded web page.

8. The method of claim 7, further comprising tracking over time a set of domains visited and analyzing individual pages to determine if they represent a search result or a product web page, based on known web page patterns.

9. The method of claim 8, further comprising leveraging known URL formats and page structure and textual patterns and keeping track of the number of times a search has been executed and the set of sites on which the search was executed and identifying product views on merchant sites and obtaining product details web pages and additional information from public web services engines.

10. The method of claim 9, further comprising tracking a set of sites on which said product was viewed and for each site tracking how many times said user visited a product on said site, the last date of visit, the number of active visits in which said user interacted with a page at said site by scrolling or clicking, and the number of times the product has been added to a merchant'"'"'s virtual cart.

11. The method of claim 7, further comprising identifying user credentials from web pages and cookies to attribute searches and product views to a specific user.

12. The method of claim 7, further comprising calculating which products said user is actively interested in purchasing by scoring each product according to the following formula:

13. The method of claim 10, further comprising using collected information to determine what categories of products said user typically shops for and a set of merchants frequented in order to drive recommendations in the form of offers related to relevant products or merchants.

14. A method of determining when a user is in-market for a specific product and knowing the general shopping preferences and habits of users, comprising:

\

identifying in-market purchasing interests and representing them as a user goal, and wherein said goal has a timeline, and goal satisfaction can be identified via a variety of contextual inputs selected from the group consisting of;

location;

traces from online shopping activity;

credit card bills;

or a pay-by-phone transactions.

15. The method of claim 14, further comprising breaking down activities into sub activities to create recommendations, and wherein identifying these different sub-activities is performed through the use of different types of sensors and their derived context, and wherein a series of these will be created and rated according to a state of said user during each of these activities.

16. The method of claim 14, further comprising using contextual clues to determine when said goal is related to said user or someone else and creating a profile that is segmented with a primary segment relating to said user directly and other segments relating to other people or activities related to said user.

17. A computer readable medium encoded with computer executable instructions, which when accessed, cause a machine to perform operations comprising:

capturing context information of a user and using heuristics based on a common knowledge database to turn location tracked over time combined with said context information into semantic location information.

18. The computer readable medium of claim 17, further comprising additional instructions causing said machine to perform further operations including creating and identifying said heuristics.

19. The computer readable medium of claim 18, wherein trace data of GPS coordinates are obtained continuously throughout said user'"'"'s day and first clustered to identify interesting locations, then up-leveled to a street address or business name and then semantically interpreted in one of several categories.

20. The computer readable medium of claim 19, wherein said heurists are used to semantically interpret said clustered locations upleveled to a street address or business name, into one of several categories.

21. The computer readable medium of claim 20, further comprising additional instructions causing said machine to perform further operations including further refining categories into a set of daily patterns.

22. The computer readable medium of claim 17, further comprising additional instructions causing said machine to perform further operations further comprising distributing said semantic location information to a service provider, wherein said service provider provides an incentive to said user for said context information.

23. A computer readable medium encoded with computer executable instructions, which when accessed, cause a machine to perform operations, comprising, determining when a user is in-market for a specific product and knowing the general shopping preferences and habits of users by using web browsing behavior to determine a product said user is interested in purchasing and how said user typically likes to shop;

and

wherein said web browsing behavior is determined by watching some or all web pages loaded and analyzing at least URLs, page text, and cookies associated with loaded web pages.

24. The computer readable medium of claim 23, further comprising additional instructions causing said machine to perform further operations including tracking over time a set of domains visited and analyzing individual pages to determine if they represent a search result or a product web page, based on known web page patterns.

25. The computer readable medium of claim 24, further comprising additional instructions causing said machine to perform further operations further comprising leveraging known URL formats and page structure and textual patterns and keeping track of the number of times a search has been executed and the set of sites on which the search was executed and identifying product views on merchant sites and obtaining product details web pages and additional information from public web services engines.

26. The computer readable medium of claim 25, further comprising additional instructions causing said machine to perform further operations further comprising tracking a set of sites on which said product was viewed and for each site tracking how many times said user visited a product on said site, the last date of visit, the number of active visits in which said user interacted with a page at said site by scrolling or clicking, and the number of times the product has been added to a merchant'"'"'s virtual cart.

27. The computer readable medium of claim 23, further comprising additional instructions causing said machine to perform further operations further comprising identifying user credentials from web pages and cookies to attribute searches and product views to a specific user.

28. The computer readable medium of claim 23, further comprising additional instructions causing said machine to perform further operations further comprising calculating which products said user is actively interested in purchasing by scoring each product according to the following formula:

29. The computer readable medium of claim 28, further comprising additional instructions causing said machine to perform further operations further comprising using collected information to determine what categories of products said user typically shops for and a set of merchants frequented in order to drive recommendations in the form of offers related to relevant products or merchants.

30. The computer readable medium of claim 23, further comprising additional instructions causing said machine to perform further operations further comprising identifying in-market purchasing interests and representing them as a user goal, and wherein said goal has a timeline, and goal satisfaction can be identified via a variety of contextual inputs selected from the group consisting of:

location;

traces from online shopping activity;

credit card bills;

or a pay-by-phone transactions.

31. The computer readable medium of claim 23, further comprising additional instructions causing said machine to perform further operations further comprising breaking down activities into sub activities to create recommendations, and wherein identifying these different sub-activities is performed through the use of different types of sensors and their derived context, and wherein a series of these will be created and rated according to a state of said user during each of these activities.

32. The computer readable medium of claim 30, further comprising additional instructions causing said machine to perform further operations further comprising using contextual clues to determine when said goal is related to said user or someone else and creating a profile that is segmented with a primary segment relating to said user directly and other segments relating to other people or activities related to said user.

33. A system, comprising:

an information assimilation and communication platform adapted to capture context information of a user and use heuristics based on a common knowledge database to turn location tracked over time combined with said context information into semantic location information.

34. The system of claim 33, wherein said platform creates and identifies said heuristics.

35. The system of claim 34, wherein trace data of GPS coordinates are obtained continuously throughout said user'"'"'s day and first clustered to identify interesting locations, then up-leveled to a street address or business name and then semantically interpreted in one of several categories.

36. The system of claim 35, wherein said heurists are used to semantically interpret said clustered locations upleveled to a street address or business name, into one of several categories.

37. The system of claim 36, wherein said platform further refines categories into a set of daily patterns.

38. The system of claim 33, wherein said platform is capable of distributing said semantic location information to a service provider, wherein said service provider provides an incentive to said user for said context information.

39. A system, comprising:

an information assimilation and communication platform capable of determining when a user is in-market for a specific product and knowing the general shopping preferences and habits of users by using web browsing behavior to determine the products said user is interested in purchasing and how said user typically likes to shop; and

wherein said web browsing behavior is determined by watching all web pages loaded and analyzing a URL, page text, and cookies associated with each loaded web page.

40. The system of claim 39, wherein said platform is further capable of tracking over time a set of domains visited and analyzing individual pages to determine if they represent a search result or a product web page, based on known web page patterns.

41. The system of claim 40, wherein said platform if further capable of leveraging known URL formats and page structure and textual patterns and keeping track of the number of times a search has been executed and the set of sites on which the search was executed and identifying product views on merchant sites and obtaining product details web pages and additional information from public web services engines.

42. The system of claim 41, wherein said platform is further capable of tracking a set of sites on which said product was viewed and for each site tracking how many times said user visited a product on said site, the last date of visit, the number of active visits in which said user interacted with a page at said site by scrolling or clicking, and the number of times the product has been added to a merchant'"'"'s virtual cart.

43. The system of claim 39, wherein said platform is further capable of identifying user credentials from web pages and cookies to attribute searches and product views to a specific user.

44. The system of claim 39, wherein said platform is further capable of calculating which products said user is actively interested in purchasing by scoring each product according to the following formula:

45. They system of claim 44, wherein said platform is further capable of using collected information to determine what categories of products said user typically shops for and a set of merchants frequented in order to drive recommendations in the form of offers related to relevant products or merchants.

46. The system of claim 39, wherein said platform is further capable of identifying in-market purchasing interests and representing them as a user goal, and wherein said goal has a timeline, and goal satisfaction can be identified via a variety of contextual inputs selected from the group consisting of:

location;

traces from online shopping activity;

credit card bills;

or a pay-by-phone transactions.

47. The system of claim 39, wherein said platform is further capable of breaking down activities into sub activities to create recommendations, and wherein identifying these different sub-activities is performed through the use of different types of sensors and their derived context, and wherein a series of these will be created and rated according to a state of said user during each of these activities.

48. The system of claim 46, wherein said platform is further capable of using contextual clues to determine when said goal is related to said user or someone else and creating a profile that is segmented with a primary segment relating to said user directly and other segments relating to other people or activities related to said user.