METHOD AND SYSTEM FOR SELECTING TASK TEMPLATES

• US 20150007181A1
• Filed: 06/27/2013
• Published: 01/01/2015
• Est. Priority Date: 06/27/2013
• Status: Abandoned Application

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Saraschandra, Karanam, Dasgupta, Koustuv, Rajan, Vaibhav, Sampath, Harini Alagarai, Indurkhya, Bipin
• Application Number:
  US13/928,709
• Publication Number:
  US 20150007181A1
• Time in Patent Office: Days
• US Class Current: 718/100
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/06311 Scheduling, planning or task assignment for a person or group
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/10 Office automation; Time management

Abstract

Disclosed embodiments relate to systems and methods for selecting at least one task template from one or more task templates for uploading crowdsourcing tasks. Values of a pre-defined set of cognitive features associated with one or more task templates are determined. Further, a historical data is obtained based on one or more task features and one or more performance features corresponding to the one or more task templates. A statistical model selects the at least one task template based on the values of the pre-defined set of cognitive features and the historical data.

Claims

1. A method for selecting at least one task template from one or more task templates usable in one or more crowdsourcing tasks, the method comprising:

determining values of a pre-defined set of cognitive features associated with each of the one or more task templates; and

selecting the at least one task template based on the values of the pre-defined set of cognitive features and a statistical model maintained based on a historical data, wherein the historical data corresponds to at least one of one or more task features or one or more performance features associated with the one or more task templates,

wherein the method is performed by one or more processors.

2. The method of claim 1, wherein the pre-defined set of cognitive features comprises at least one of a saliency metric (X_Sal), a search efficiency metric (X_SearchEff), a target-distracter similarity (X_TDSim), a distracter-distracter similarity (X_DDSim), a task description similarity (X_TaskDesSim), a domain knowledge metric (X_DK) metric, or a working memory metric (X_WM).

3. The method of claim 1 further comprising uploading one or more sample crowdsourcing tasks corresponding to each of the one or more task templates to obtain the historical data.

4. The method of claim 1 further comprising predicting the one or more performance features of the one or more task templates for the one or more crowdsourcing tasks.

5. The method of claim 4, wherein the statistical model uses a regression technique to predict the one or more performance features of the one or more task templates.

6. The method of claim 1 further comprising updating the statistical model based on at least one of the one or more task features, the one or more performance features, or the values of the pre-defined set of cognitive features associated with the one or more task templates.

7. The method of claim 1, wherein the one or more task features comprise at least one of a day of submitting a crowdsourcing task, a time in the day of submitting the crowdsourcing task, a cost of the crowdsourcing task, or a country of crowdworker.

8. The method of claim 1, wherein the one or more performance features comprise at least one of an accuracy rate of a crowdsourcing task, a response time of the crowdsourcing task, or a completion rate of the crowdsourcing task.

9. The method of claim 1, wherein a crowdsourcing task comprises at least one of handwriting recognition, image labeling, language translation, or video labeling.

10. A method for predicting one or more performance features of one or more task templates usable in one or more crowdsourcing tasks, the method comprising:

receiving the one or more task templates corresponding to the one or more crowdsourcing tasks;

determining values of a pre-defined set of cognitive features associated with each of the one or more task templates;

determining a statistical relationship between the values of the pre-defined set of cognitive features and a historical data using a statistical model, wherein the historical data corresponds to at least one of one or more task features or the one or more performance features associated with the one or more task templates;

predicting the one or more performance features of the one or more task templates based on the determined statistical relationship; and

updating the statistical model based on at least one of the one or more task features, the one or more performance features, or the values of the pre-defined set of cognitive features associated with the one or more task templates,

wherein the method is performed by one or more processors.

11. A system for selecting at least one task template from one or more task templates usable in one or more crowdsourcing tasks, the system comprising:

a memory comprising;

a communication manager configured to receive the one or more task templates corresponding to the one or more crowdsourcing tasks;

a feature determination module configured to determine values of a pre-defined set of cognitive features associated with each of the one or more task templates; and

a statistical model configured to select the at least one task template based on the values of the pre-defined set of cognitive features and a historical data, wherein the historical data corresponds to at least one of one or more task features or one or more performance features associated with the one or more task templates, and

one or more processors configured to execute the communication manager, the feature determination module, and the statistical model.

12. The system of claim 11, wherein the pre-defined set of cognitive features comprises at least one of a saliency metric (X_Sal), a search efficiency metric (X_SearchEff), a target-distracter similarity (X_TDSim), a distracter-distracter similarity (X_DDSim), a task description similarity (X_TaskDesSim), a domain knowledge metric (X_DK), or a working memory metric (X_WM).

13. The system of claim 11, wherein the historical data is obtained based on one or more sample crowdsourcing tasks corresponding to each of the one or more task templates.

14. The system of claim 11 further comprising a training module configured to create the statistical model based on at least one of the one or more task features or the one or more performance features associated with the one or more task templates.

15. The system of claim 14, wherein the training module is further configured to update the statistical model based on at least one of the one or more task features, the one or more performance features, or the values of the pre-defined set of cognitive features associated with the one or more task templates.

16. The system of claim 11 further comprising a template manager configured to identify the pre-defined set of cognitive features in the one or more task templates.

17. The system of claim 11, wherein the statistical model is further configured to predict the one or more performance features of the one or more task templates for the one or more crowdsourcing tasks.

18. The system of claim 17, wherein the statistical model uses a regression technique to predict the one or more performance features of the one or more task templates.

19. The system of claim 11, wherein the one or more task features comprise at least one of a day of posting a crowdsourcing task, a time in the day of posting the crowdsourcing task, a cost of the crowdsourcing task, or a country of crowdworker.

20. A computer program product for use with a computer, the computer program product comprising a non-transitory computer readable medium, the non-transitory computer readable medium stores a computer program code for selecting at least one task template from one or more task templates usable in one or more crowdsourcing tasks, the computer program code performing a method, the method comprising:

determining values of a pre-defined set of cognitive features associated with each of the one or more task templates; and

selecting the at least one task template based on the values of the pre-defined set of cognitive features and a statistical model maintained based on a historical data, wherein the historical data corresponds to at least one of one or more task features or one or more performance features associated with one or more task templates.