Task Recommendation Matching via Segmented Human-Automation
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Solution Overview
Problem
Existing systems face challenges in efficiently matching human performance tasks with appropriate task performers, as they struggle to identify and assign tasks effectively due to the cognitive and mental capabilities of humans that are difficult to encode in automated programs, leading to issues in ensuring timely and skilled task performance.
Innovation Solution
An electronic marketplace is developed that facilitates interactions between task requesters and performers, using automated matching to recommend tasks based on relevance scores generated from various user and task attributes, qualifications, and performance histories, enabling efficient task assignment and performance tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated programs are used to perform tasks, then productivity and speed are improved, but the system cannot handle complex cognitive and mental tasks that require human judgment and perception
Solution Approach 1:
The system segments tasks into two categories: automated tasks that can be performed by computer programs and human performance tasks that require human cognitive abilities. This segmentation allows the system to leverage automated processing for routine operations while directing complex tasks to human performers, thus maintaining both high productivity and adaptability.
Solution Approach 2:
The patent introduces an intermediary electronic marketplace that acts as a mediator between task requesters and human task performers. This intermediary system receives automated program requests, identifies suitable human performers based on qualifications and availability, and facilitates the assignment process, thereby bridging the gap between automated systems and human cognitive capabilities.
2Reliability
If human task performers are used for cognitive tasks, then task quality and judgment are improved, but the difficulty of identifying and assigning appropriate performers increases
Solution Approach 1:
Human task performers create detailed profiles that automatically advertise their qualifications, skills, and availability. The system enables performers to self-manage their task assignments by receiving automated notifications of suitable tasks based on their profile characteristics, reducing the complexity of manual assignment processes while ensuring high-quality matching.
Solution Approach 2:
The system implements feedback mechanisms where task performers provide information about their qualifications and performance outcomes. This feedback is used by the automated matching system to refine task assignments, ensuring that high-quality performers are consistently selected for appropriate tasks while simplifying the overall assignment process through data-driven decision-making.
3Reliability
If manual task assignment is used, then task performer expertise is optimized, but the time required to identify and assign tasks increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing detailed profiles of human task performers that include their qualifications, skills, and availability. When tasks are submitted, the automated system can quickly match them with suitable performers based on these pre-existing profiles, eliminating the need for time-consuming manual search and evaluation processes while maintaining expert-level matching accuracy.
Data Source
AI summary
Techniques are described for facilitating interactions between task requesters who have tasks that are available to be performed and task performers who are available to perform tasks, such as via an electronic marketplace acting as an intermediary for task performance. In some situations, the facilitating of the interactions includes automatically matching available tasks to task performer users, such as to automatically generate recommendations for task performer users of available tasks that are appropriate for those task performer users to perform. Such generated task recommendations for task performer users may then be provided to those task performer users in various ways, including via one or more Web pages or electronic communications sent to devices of the task performer users. The task recommendations may be generated in various ways, including based on previous tasks performed by the task performer users and on other prior activities of the task performer users.