IP Library › Granted Patent US 11,243,671

Granted Patent B2

US 11,243,671 · App. 16/404,453 · Granted Feb 8, 2022

Methods and systems for soliciting an answer to a question

Inventors: Adam Edward D'Angelo (Mountain View, CA); Abhinav Sharma (San Francisco, CA); Muhammad Emmad Mazhari (San Francisco, CA); David Cole (Los Altos, CA)

Assignee: QUORA, INC.

G06F3/0484 G06F3/0482 G09B7/02

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Quick Facts

Patent No.: US 11,243,671
App. No.: 16/404,453
Granted: Feb 8, 2022
Kind: B2

External Links

USPTO Patent Center↗ Full Patent PDF↗

Abstract

A question-and-answer application with an “ask-to-answer” feature is described. The ask-to-answer feature enables any user to solicit an answer to a question from one or more users. Upon soliciting one or more users for an answer to a particular question, a message may be directed to the one or more users. The message may include a copy of the text of the question and may provide a mechanism enabling the one or more users to pass on answering the question. Subsequent to the solicitation, the question page may include a notification with information about the solicitation, including information identifying the group of users who have been asked to answer the question and the number of times the one or more users has been asked to provide an answer.

Claims (89)

1. A computer-implemented method comprising:

receiving user input associated with a requesting user, the user input including a solicitation for an answer to a question and one or more factors for generating a ranked list of recommended users to provide the answer;

training a first machine-learning model to generate a predicted quality score of an answer that a user will provide;

generating, by the first machine-learning model, predicted quality scores for a list of recommended users based on the one or more factors, the predicted quality scores usable to generate a ranked list of recommended users;

receiving a selected list of recommended users, wherein the selected list of recommended users is selected from the ranked list of recommended users based on the predicted quality scores of the list of recommended users;

predicting, by a second machine-learning model, a likelihood that a recommended user of the selected list of recommended users will provide an answer to the question if solicited by a notification, wherein the likelihood is used to define a subset of the selected list of recommended users;

facilitating a communication, via a notification, to the subset of the selected list of recommended users, the communication including the question; and

presenting a webpage that includes the question, and an indication that the subset of the selected list of recommended users have been solicited to answer the question.

2. The computer-implemented method of claim 1 , wherein the communication to the subset of the selected list of recommended users includes information identifying the requesting user.

3. The computer-implemented method of claim 1 , wherein the indication included with the webpage includes information identifying the requesting user.

4. The computer-implemented method of claim 1 , wherein the indication included with the webpage includes information identifying all users who have solicited the selected list of recommended users for an answer to the question.

5. The computer-implemented method of claim 1 , further comprising:

presenting to the requesting user information identifying one or more groups of users to whom the requesting user can direct a solicitation for an answer to the question.

6. The computer-implemented method of claim 5 , wherein the information identifying the one or more groups of users includes one or more interest groups presented in a list that is dynamically populated in real time in response to the user input associated with the requesting user, the one or more interest groups in the list matching some portion of the user input, and wherein the receiving the selected list of recommended users includes detecting a selection of a user in the selected list of recommended users.

7. The computer-implemented method of claim 5 , wherein the information identifying the one or more groups of users includes one or more interest groups to whom the requesting user has a relationship.

8. The computer-implemented method of claim 7 , wherein the relationship is a unilaterally defined relationship.

9. The computer-implemented method of claim 7 , wherein the relationship is a bilaterally defined relationship.

10. The computer-implemented method of claim 5 , wherein the information identifying the one or more groups of users includes descriptions of backgrounds for groups of users to whom the requesting user can direct a solicitation for an answer to the question.

11. The computer-implemented method of claim 1 , further comprising:

subsequent to the communication of the question to the subset of the selected list of recommended users, detecting an event indicating that one or more recommended users of the subset of the selected list of recommended users has passed on responding to the solicitation for an answer to the question; and

communicating a message to the requesting user, the message indicating that the one or more recommended users of the subset of the selected list of recommended users has passed on responding to the solicitation for an answer to the question.

12. The computer-implemented method of claim 1 , further comprising:

communicating a message to the requesting user, the message indicating that the one or more recommended users of the subset of the selected list of recommended users has provided an answer to the question.

13. The computer-implemented method of claim 12 , further comprising:

upon detecting an event indicating that one or more recommended users of the subset of the selected list of recommended users has provided an answer to the question, converting the solicitation for an answer to the question to a vote by the requesting user for the answer to the question provided by the one or more recommended users of the subset of the selected list of recommended users.

14. The computer-implemented method of claim 1 , further comprising:

subsequent to the communication of the question to the subset of the selected list of recommended users, detecting an event indicating a recommended user from the subset of the selected list of recommended users has provided an answer to the question; and

communicating a message to the requesting user, the message indicating that the recommended user from the one or more recommended users of the subset of the selected list of recommended users has provided an answer to the question.

15. A system comprising:

one or more processors; and

a non-transitory computer-readable medium including instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including:

training a first machine-learning model to generate a predicted quality score of an answer that a user will provide;

facilitating a communication, via a notification, to the subset of the selected list of recommended users, the communication including the question; and

presenting a webpage that includes the question, and an indication that the subset of the selected list of recommended users have been solicited to answer the question.

16. The system of claim 15 , wherein the communication to the subset of the selected list of recommended users includes information identifying the requesting user.

17. The system of claim 15 , wherein the indication included with the webpage includes information identifying the requesting user.

18. The system of claim 15 , wherein the indication included with the webpage includes information identifying all users who have solicited the selected list of recommended users for an answer to the question.

19. The system of claim 15 , wherein the operations further include:

presenting to the requesting user information identifying one or more groups of users to whom the requesting user can direct a solicitation for an answer to the question.

20. A non-transitory computer-readable medium having stored thereon instructions that, when executed by one or more processors, cause the one or more processors to perform operations including:

training a first machine-learning model to generate a predicted quality score of an answer that a user will provide;

facilitating a communication, via a notification, to the subset of the selected list of recommended users, the communication including the question; and

presenting a webpage that includes the question, and an indication that the subset of the selected list of recommended users have been solicited to answer the question.

21. The system of claim 19 , wherein the information identifying the one or more groups of users includes one or more interest groups presented in a list that is dynamically populated in real time in response to the user input associated with the requesting user, the one or more interest groups in the list matching some portion of the user input, and wherein the receiving the selected list of recommended users includes detecting a selection of a user in the selected list of recommended users.

22. The system of claim 19 , wherein the information identifying the one or more groups of users includes one or more interest groups to whom the requesting user has a relationship.

23. The system of claim 22 , wherein the relationship is a unilaterally defined relationship.

24. The system of claim 22 , wherein the relationship is a bilaterally defined relationship.

25. The system of claim 19 , wherein the information identifying the one or more groups of users includes descriptions of backgrounds for groups of users to whom the requesting user can direct a solicitation for an answer to the question.

26. The system of claim 15 , further comprising:

27. The system of claim 15 , further comprising:

28. The system of claim 27 further comprising:

29. The system of claim 15 , further comprising:

30. The non-transitory computer-readable medium of claim 20 , wherein the communication to the subset of the selected list of recommended users includes information identifying the requesting user.

31. The non-transitory computer-readable medium of claim 20 , wherein the indication included with the webpage includes information identifying the requesting user.

32. The non-transitory computer-readable medium of claim 20 , wherein the indication included with the webpage includes information identifying all users who have solicited the selected list of recommended users for an answer to the question.

33. The non-transitory computer-readable medium of claim 20 , wherein the instructions further cause the one or more processors to perform operations including:

presenting to the requesting user information identifying one or more groups of users to whom the requesting user can direct a solicitation for an answer to the question.

34. The non-transitory computer-readable medium of claim 33 , wherein the information identifying the one or more groups of users includes one or more interest groups presented in a list that is dynamically populated in real time in response to the user input associated with the requesting user, the one or more interest groups in the list matching some portion of the user input, and wherein the receiving the selected list of recommended users includes detecting a selection of a user in the selected list of recommended users.

35. The non-transitory computer-readable medium of claim 33 , wherein the information identifying the one or more groups of users includes one or more interest groups to whom the requesting user has a relationship.

36. The non-transitory computer-readable medium of claim 35 , wherein the relationship is a unilaterally defined relationship.

37. The non-transitory computer-readable medium of claim 35 , wherein the relationship is a bilaterally defined relationship.

38. The non-transitory computer-readable medium of claim 33 , wherein the information identifying the one or more groups of users includes descriptions of backgrounds for groups of users to whom the requesting user can direct a solicitation for an answer to the question.

39. The non-transitory computer-readable medium of claim 20 , wherein the instructions further cause the one or more processors to perform operations including:

40. The non-transitory computer-readable medium of claim 20 , wherein the instructions further cause the one or more processors to perform operations including:

41. The non-transitory computer-readable medium of claim 40 , wherein the instructions further cause the one or more processors to perform operations including:

42. The non-transitory computer-readable medium of claim 20 , wherein the instructions further cause the one or more processors to perform operations including:

Assignments (1)

ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 6, 2019

From: D'ANGELO, ADAM EDWARD; SHARMA, ABHINAV; MAZHARI, MUHAMMAD EMMAD; COLE, DAVID

To: QUORA, INC.

Reel/Frame 049398/0695 →

Continuity (2)

Provisional Application 62668140 · May 7, 2018

Related Publication 20190339832A1 · Nov 7, 2019