Determining incentive for crowd sourced question

• US 9,865,001 B2
• Filed: 06/12/2015
• Issued: 01/09/2018
• Est. Priority Date: 12/03/2014
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Bohra, Amit P., Kummamuru, Krishna, Shivkumar, Abhishek
• Primary Examiner(s): Reagan, James A
• Application Number:
  US14/738,100
• Publication Number:
  US 20160162922A1
• Time in Patent Office: 942 Days
• Field of Search: 705 1411
• CPC Class Codes: G06Q 10/101 Collaborative creation, e.g. joint development of products or services
• CPC Class Codes: G06Q 30/0208 Trade or exchange of goods or services in exchange for incentives or rewards
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: G09B 19/00 Teaching not covered by other main groups of this subclass teaching or practice apparatus for gun-aiming or gun-laying F41G3/26
• CPC Class Codes: G09B 7/02 of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student

Abstract

A computing device receives one or more questions. The computing device determines at least one of an urgency factor, an importance factor, and a difficulty level associated with the received one or more questions and determines at least one of an availability factor, a skill factor, and a quality level associated with the received one or more questions. The computing device determines an incentive based on the determined at least one of an urgency factor, an importance factor, and a difficulty level associated with the received one or more questions, and the determined at least one of an availability factor, a skill factor, and a quality level associated with the received one or more questions.

Claims

1. A method for determining a payment for one or more questions comprising:

receiving one or more questions;

determining at least one of topics associated with the received one or more questions by utilizing natural language processing;

determining at least one of an urgency factor, an importance factor, and a difficulty level associated with the received one or more questions, wherein the difficulty level is determined by comparing the at least one of topics associated with the one or more questions to previously received questions and assigning the difficulty of the previously received questions to the received one or more questions;

determining at least one of an availability factor of at least one of experts, a skill factor of the at least one of the experts, and a quality level associated with the received one or more questions, wherein the availability factor is determined by a number of previously received answers to previously received questions by the at least one of the experts, wherein the skill factor is determined by analyzing a profile of the at least one of the experts, and wherein the quality level is determined by a rating of the at least one of the experts to the previously received answers; and

determining the payment based on adding up the determined at least one of the urgency factor multiplied by a weight associated with the urgency factor, the importance factor multiplied by a weight associated with the importance factor, the difficulty level associated with the received the one or more questions multiplied by a weight associated with the difficulty level, the determined at least one of the availability factor multiplied by a weight associated with the availability factor, the skill factor multiplied by a weight associated with the skill factor, and the quality level associated with the received one or more questions multiplied by a weight associated with the quality level.

2. The method of claim 1, wherein the difficulty level is further determined based on at least one of one or more topics present within the one or more questions and a number of nouns present within the one or more questions.

3. The method of claim 1, wherein the availability level is further determined based on at least one of an activity level of an expert, a login frequency associated with an expert, one or more previous questions asked to an expert, and a login schedule of an expert.

4. The method of claim 1, further comprising the computing device determining a recency factor associated with the one or more questions.

5. The method of claim 4, wherein the payment is further based on the determined recency factor associated with the one or more questions.

6. The method of claim 1, wherein the skill factor is further determined for a group of available experts.

7. The method of claim 1, wherein a quality level is further determined based on a rating associated with one or more answers submitted for one or more substantially similar questions.