Dynamic personalization profile

• US 9,098,866 B1
• Filed: 11/06/2012
• Issued: 08/04/2015
• Est. Priority Date: 11/09/2011
• Status: Active Grant

• Current Assignee: Michael Gurin
• Original Assignee: Michael Gurin
• Inventors: Gurin, Michael
• Primary Examiner(s): Gaffin, Jeffrey A
• Assistant Examiner(s): Bharadwaj, Kalpana
• Application Number:
  US13/669,827
• Time in Patent Office: 1,001 Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/109 Time management, e.g. calendars, reminders, meetings or time accounting
• CPC Class Codes: G06Q 30/0256 User search
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0271 Personalized advertisement

Abstract

The present invention generally relates to utilization of at least two known location addresses yielding accurate determination of a user'"'"'s mode of operations and/or preferences. In one embodiment, the present invention utilizes proximity and/or vector between a current location address and a known home or work address to determine preferences that dynamically change based on the current location and inter-relationship between the current location and at least one other known location address.

Claims

1. A decision engine comprising a first user, the first user having a user personalization profile and a first user current location, wherein the first user has an at least one sub-segment location further comprised of a location segmented profile as a child of the at least one sub-segment location, wherein the first user personalization profile has at least one sub-segment within the first user personalization profile that is a function of at least one sub-segment location including a home address or a work address, and wherein the first user personalization profile is a function of the combination of the first user current location distance to the at least one sub-segment location of the first user personalization profile.

2. The decision engine according to claim 1 wherein the first user personalization profile has an at least one sub-segment mode further comprised of a mode segmented profile as a child of the at least one sub-segment mode, wherein the at least one sub-segment mode within the first user personalization profile is a function of the segmented first user personalization profile, and wherein the at least one sub-segment mode is determined by having a mode probability as a function of a current distance between the current location and the at least one second location.

3. The decision engine according to claim 2 wherein the at least one sub-segment location has at least one geofence and whereby the at least one geofence is included or excluded in the mode probability as a function of the at least one sub-segment mode.

4. The decision engine according to claim 1 further comprised of a database having an occurrence frequency for an instance of the first user the first user current location, and a first user mode of the first user personalization profile having a mode probability as a function of the occurrence frequency at the first user current location.

5. The decision engine according to claim 1 further comprised of a database having a time-of-day probability for the at least one sub-segment location wherein the first user personalization profile mode probability is further a function of the time-of-day probability at the current location.

6. The decision engine according to claim 1 further comprised of a at least one sub-segment mode, a database having a scheduled event, and wherein the the at least one sub-segment mode probability is further a function of the scheduled event.

7. The decision engine according to claim 1 further comprised of a at least one sub-segment mode, a database having a activity function type, and wherein the at least one sub-segment mode probability is further a function of the activity function type.

8. The decision engine according to claim 1 further comprised of a target message engine operable to generate a signal including an audio, text, or graphical signal as a method of communicating to the first user.

9. A decision engine comprising a first user, the first user having a user personalization profile and first user current location, an at least one second user having a second user current location, wherein the first user personalization profile has an at least one sub-segment location having a segmented profile as a child of the at least one sub-segment location, and wherein the user personalization profile at least sub-segment location is a function of the combination of the first user current location and the at least one second user current location.

10. The decision engine according to claim 9, wherein the first user personalization profile has an at least one sub-segment mode further comprised of a mode segmented profile as a child of the at least one sub-segment mode, and wherein the at least one sub-segment mode has a mode probability as a function of distance between the first user current location and the at least one second user current location.

11. The decision engine according to claim 9 further comprised of a first user personalization profile having an at least one sub-segment mode having a segmented profile as a child of the at least one sub-segment mode, database having a set of child relationships between the first user and the second user and wherein the mode is determined by a mode probability that is further a function of the child relationship.

12. The decision engine according to claim 11 wherein the at least one second current location has at least one geofence and whereby the at least one geofence is included or excluded in the at least one sub-segment location probability as a function of the of the first user personalization profile.

13. The decision engine according to claim 9 further comprised of an at least one sub-segment mode having a segmented profile as a child of the at least one sub-segment mode, and wherein the user personalization profile at least sub-segment mode has a database having an occurrence frequency for an instance of first user being in the least one sub-segment mode, and the at least one sub-segment mode having a probability as a function of the occurrence frequency at the least one sub-segment mode.

14. The decision engine according to claim 9 further comprised of an at least one sub-segment mode having a segmented profile as a child of the at least one sub-segment mode, and wherein the user personalization profile at least sub-segment mode has a database having a time-of-day probability for the at least sub-segment mode wherein the at least sub-segment mode probability is further a function of the time-of-day at the first user current location.

15. The decision engine according to claim 9 further comprised of a database having a scheduled event wherein the at least sub-segment mode probability is further a function of the scheduled event.

16. The decision engine according to claim 15 further comprised of a database having a activity function type wherein the at least sub-segment mode probability is further a function of the activity function type.

17. The decision engine according to claim 9 further comprised of a target message engine operable to generate a signal including an audio, text, or graphical signal as a method of communicating to the first user.

18. A decision engine comprising a first user, the first user having a user personalization profile, a database having a set of child relationships between the first user and an at least one second user, a database of scheduled events in which the at least one second user is currently attending the scheduled events, an at least one sub-segment mode having a segmented profile as a child of the at least one sub-segment mode having a mode probability for the at least one sub-segment mode wherein the at least one sub-segment mode is a function of the first user, the child relationship between the first user and the at least one second user, and the scheduled event in which at least one of the second user is attending.

19. The decision engine according to claim 18 further comprised of a database having an activity function type wherein the at least one sub-segment mode probability is further a function of the activity function type.

20. The decision engine according to claim 18 further comprised of a target message engine operable to generate a signal including an audio, text, or graphical signal as a method of communicating to at least one of the first user or the at least one second user.