TECHNIQUES TO IDENTIFY IN-MARKET PURCHASING INTERESTS

• US 20110246214A1
• Filed: 06/16/2011
• Published: 10/06/2011
• Est. Priority Date: 12/15/2009
• Status: Abandoned Application

• Current Assignee: Chieh-Yih Wan, Lenitra M. Durham, Mark D. Yarvis, Philip Muse, Rita H. Wouhaybi, Sai P. Balasundaram, Sangita Sharma
• Original Assignee: Chieh-Yih Wan, Lenitra M. Durham, Mark D. Yarvis, Philip Muse, Rita H. Wouhaybi, Sai P. Balasundaram, Sangita Sharma
• Inventors: Yarvis, Mark D., Wan, Chieh-Yih, Sharma, Sangita, Balasundaram, Sai P., Wouhaybi, Rita H., Muse, Philip, Durham, Lenitra M.
• Application Number:
  US13/162,098
• Publication Number:
  US 20110246214A1
• Time in Patent Office: Days
• US Class Current: 705/1.1
• CPC Class Codes: G06Q 30/00 Commerce

Abstract

An embodiment of the present invention provides a method, comprising identifying in-market purchasing interests and representing them as a user goal to determine when a user is in-market for a specific product and to determine general shopping preferences and habits of the user, and wherein the 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.

Claims

1. A method, comprising:

identifying in-market purchasing interests and representing them as a user goal to determine when a user is in-market for a specific product and to determine general shopping preferences and habits of said user; 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.

2. The method of claim 1, further comprising breaking down activities into sub activities to create recommendations, and wherein identifying different said sub-activities is performed through the use of different types of sensors and their derived context, and wherein a series of said sub-activities will be created and rated according to a state of said user during each of said sub-activities.

3. The method of claim 1, 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.

4. A non-transitory computer readable medium encoded with computer executable instructions, which when accessed, cause a machine to perform operations, comprising

identifying in-market purchasing interests and representing them as a user goal to determine when a user is in-market for a specific product and to determine general shopping preferences and habits of said user;

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.

5. The non-transitory computer readable medium encoded with computer executable instructions, which when accessed, cause a machine to perform operations claim 4, further comprising breaking down activities into sub activities to create recommendations, and wherein identifying different said sub-activities is performed through the use of different types of sensors and their derived context, and wherein a series of said sub-activities will be created and rated according to a state of said user during each of said sub-activities.

6. The non-transitory computer readable medium encoded with computer executable instructions, which when accessed, cause a machine to perform operations claim 5, 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.

7. A system, comprising:

an information assimilation and communication platform capable of identifying in-market purchasing interests and representing them as a user goal to determine when a user is in-market for a specific product and to determine general shopping preferences and habits of said user; 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.

8. The system of claim 7, wherein said platform is further capable of breaking down activities into sub activities to create recommendations, and wherein identifying different said sub-activities is performed through the use of different types of sensors and their derived context, and wherein a series of said sub-activities will be created and rated according to a state of said user during each of said sub-activities.

9. The system of claim 8, 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.

10. An apparatus, comprising:

a mobile device capable of identifying in-market purchasing interests and representing them as a user goal to determine when a user of said mobile device is in-market for a specific product and to determine general shopping preferences and habits of said user; 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.

11. The apparatus of claim 10, wherein said mobile device is further capable of breaking down activities into sub activities to create recommendations, and wherein identifying different said sub-activities is performed through the use of different types of sensors and their derived context, and wherein a series of said sub-activities will be created and rated according to a state of said user during each of said sub-activities.

12. The apparatus of claim 11, wherein said mobile device 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.