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A System and Method for Enterprise Turking Service Disclosure Number: IPCOM000230927D
Publication Date: 2013-Sep-18
Document File: 2 page(s) / 44K

Publishing Venue

The Prior Art Database


The idea is to develop a service for "Enterprise Turking" - crowd-sourcing the completion of a large number of small tasks -- that can be hooked on to any other Social Engagement Platform (SEP) within an enterprise for the purpose of facilitating time-critical and high-quality contributions from experts by allocating appropriate reward points to them. This turking service can be leveraged to design, manage, and track the incentive program for a wide range of heterogeneous social engagement platforms in a seamless manner.

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A System and Method for Enterprise Turking Service

Disclosed is a system to realize such a turking service. As shown in the Figure 1 below, this system perceives any given SEP as an oracle comprising a subset of the following entities:

. Every SEP is assumed to have a predefined set of

which need to be supported via incentive program. The nature of

can vary widely within and across SEPs. An

is a user of a SEP who performs a

. There is also a notion of

. The task ownership may be either with a user or with SEP itself. It is assumed that SEP has a well defined set of

. It is the

who (optionally) sets the

amount for the task and finally the disclosed turking system decides the

as the number of reward points to give to an

who completes a

on behalf of

. The

amount can either be the same as the

amount or less than the

amount, depending on the context and a predefined set of

for a SEP. There might be a notion of

associated with a

and this may be used as a constraint while deciding the reward point by the proposed turking system. Further, there might also be a notion of

which measures the quality of the task done by an

and can also be used as a constraint by the proposed turking system.

At least one embodiment of the disclosed system includes the feature that can assist a

to set the right amount of

in order to maximize the likelihood of a quality result. This is achieved by mining the historical data of the underlying SEP and learning a response curve by means of employ...