IP Library Granted Patent US 9,189,139
Granted Patent B2
US 9,189,139 · App. 13/901,380 · Granted Nov 17, 2015

Likelihood-based personalized navigation system and method

Inventors: Tomer Cohen (Mountain View, CA); Leah M. M. McGuire (Redwood City, CA); Akhilesh Gupta (Los Altos, CA); Kiran Prasad (Santa Clara, CA)
Assignee: LinkedIn Corporation
G06F3/04847G06F3/0481
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Quick Facts
Patent No.
US 9,189,139
App. No.
13/901,380
Granted
Nov 17, 2015
Kind
B2
Abstract

A system may be configured to provide a user interface on a mobile device for a social network having a plurality of functions. The system may include a display, configured to display a representation of each of a subset of the plurality of functions, wherein each function of the subset is implementable upon selection of a representation corresponding to the function by a user and a processor, configured to dynamically generate the subset of the plurality of functions based, at least in part, on a likelihood for each of the functions that the user will select a corresponding representation. The display may be configured to display the subset as dynamically generated.

Claims (40)

1. A system configured to provide a user interface on a mobile device for a social network having a plurality of functions, comprising:

a display, configured to display a representation of each of a subset of the plurality of functions, wherein each function of the subset is implementable upon selection of a representation corresponding to the function by a user;

a processor, configured to dynamically generate the subset of the plurality of functions based, at least in part, on a likelihood for each of the functions that the user will select a corresponding representation;

wherein the display is configured to display the subset as dynamically generated;

wherein the corresponding likelihood for each function is based on a minimum number of selections of the function;

wherein the minimum number of selections is normalized based on an expected frequency of selection over time of each of the plurality of functions; and

wherein the minimum number of selections is normalized by adjusting the minimum number of selections down from a baseline for functions of the plurality of functions that have a higher than average expected frequency and by adjusting the minimum number of selections up from the baseline for functions of the plurality of functions that have a lower than average expected frequency.

2. The system of claim 1 , wherein the processor is configured to order the subset for display of the representations based, at least in part, on a weight of the corresponding functions of the subset.

3. The system of claim 2 , wherein the weight of each function is based on the corresponding likelihood and a user command to include the function in the subset.

4. The system of claim 1 , wherein the function includes at least one activity, wherein the corresponding likelihood for each function is further based on an amount of use of the function, and wherein the amount of use of the function is based, at least in part, on a number of times the at least one activity is performed per selection of the function.

5. The system of claim 1 , wherein dynamically generating the subset includes removing a function from the subset based, at least in part, on the likelihood.

6. The system of claim 5 , wherein the minimum number of selections of the function is compared against a first threshold for adding the function to the subset and a second threshold higher than the first threshold for removing the function from the subset.

7. The system of claim 6 , wherein dynamically generating the subset includes removing the function from the subset further based, at least in part, on a user command to remove the function from the subset.

8. The system of claim 1 , dynamically generating the subset includes periodically changing the functions of the subset based on a user interaction with the social network.

9. The system of claim 8 , wherein the social network is interacted with via the user interface of the mobile device and via a user interface of a computer.

10. A non-transitory memory device, the memory device communicatively coupled to a processor and comprising instructions which, when performed on the processor, cause the processor to:

display a representation of each of a subset of the plurality of functions, wherein each function of the subset is implementable upon selection of a representation corresponding to the function by a user;

dynamically generate the subset of the plurality of functions based, at least in part, on a likelihood for each of the functions that the user will select a corresponding representation;

wherein the subset is displayed as dynamically generated;

wherein the corresponding likelihood for each function is based on a minimum number of selections of the function;

wherein the minimum number of selections is normalized based on an expected frequency over time of each of the plurality of functions; and

wherein the minimum number of selections is normalized by adjusting the minimum number of selections down from a baseline for functions of the plurality of functions that have a higher than average expected frequency and by adjusting the minimum number of selections up from the baseline for functions of the plurality of functions that have a lower than average expected frequency.

11. The memory device of claim 10 , wherein the memory device is configured to order the subset for display of the representations based, at least in part, on a weight of the corresponding functions of the subset.

12. The memory device of claim 11 , wherein the weight of each function is based on the corresponding likelihood and a user command to include the function in the subset.

13. The memory device of claim 10 , wherein the function includes at least one activity, wherein the corresponding likelihood for each function is further based on an amount of use of the function, and wherein the amount of use of the function is based, at least in part, on a number of times the at least one activity is performed per selection of the function.

14. The memory device of claim 10 , wherein dynamically generating the subset includes removing a function from the subset based, at least in part, on the likelihood.

15. The memory device of claim 14 , wherein the minimum number of selections of the function is compared against a first threshold for adding the function to the subset and a second threshold higher than the first threshold for removing the function from the subset.

16. The memory device of claim 15 , wherein dynamically generating the subset includes removing the function from the subset further based, at least in part, on a user command to remove the function from the subset.

17. The memory device of claim 10 , dynamically generating the subset includes periodically changing the functions of the subset based on a user interaction with the social network.

18. The memory device of claim 17 , wherein the social network is interacted with via the user interface of the mobile device and via a user interface of a computer.

19. A method, comprising:

causing, with a processor, a user interface to display a representation of each of a subset of the plurality of functions, wherein each function of the subset is implementable upon selection of a representation corresponding to the function by a user;

dynamically generating, with the processor, the subset of the plurality of functions based, at least in part, on a likelihood for each of the functions that the user will select a corresponding representation;

wherein the subset is displayed as dynamically generated;

wherein the corresponding likelihood for each function is based on a minimum number of selections of the function;

wherein the minimum number of selections is normalized based on an expected frequency over time of each of the plurality of functions; and

wherein the minimum number of selections is normalized by adjusting the minimum number of selections down from a baseline for functions of the plurality of functions that have a higher than average expected frequency and by adjusting the minimum number of selections up from the baseline for functions of the plurality of functions that have a lower than average expected frequency.

20. The method of claim 19 , further comprising ordering the subset for display of the representations based, at least in part, on a weight of the corresponding functions of the subset.

21. The method of claim 20 , wherein the weight of each function is based on the corresponding likelihood and a user command to include the function in the subset.

22. The method of claim 19 , wherein the function includes at least one activity, wherein the corresponding likelihood for each function is further based on an amount of use of the function, and wherein the amount of use of the function is based, at least in part, on a number of times the at least one activity is performed per selection of the function.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 1, 2017
From: LINKEDIN CORPORATION
To: MICROSOFT TECHNOLOGY LICENSING, LLC
Reel/Frame 044746/0001 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 17, 2013
From: COHEN, TOMER; MCCGUIRE, LEAH M. M.; GUPTA, AKHILESH; PRASAD, KIRAN
To: LINKEDIN CORPORATION
Reel/Frame 030871/0908 →
Continuity (3)
Continuation In Part 13853948 · Mar 29, 2013
Provisional Application 61806221 · Mar 28, 2013
Related Publication 20140298203A1 · Oct 2, 2014