Automated Team Assembly via Recommendation Engine
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Solution Overview
Problem
Existing social networking platforms are inefficient in automatically identifying and assembling relevant teams of users to solve specific problems, requiring manual and time-consuming search processes that often result in unsuitable team members.
Innovation Solution
A data processing and social networking platform that uses a recommendation engine with algorithms to automatically assemble teams based on user preferences, skill sets, and demographics, by generating personalized questions and dynamically updating recommendations in real-time to ensure the availability of suitable team members.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual search process is used to identify team members, then users can find team members with some level of effort, but the process is tedious, time-consuming, and expensive
Solution Approach 1:
The system enables users to automatically assemble their own teams by inputting problem descriptions and preferences. The platform self-serves by automatically matching users with suitable team members based on skills, availability, and preferences, eliminating the need for manual searching and administrative overhead.
Solution Approach 2:
The platform acts as an intermediary between problem-solvers and potential team members. It receives problem descriptions, automatically matches them with suitable users based on skills and preferences, and facilitates team formation without requiring direct manual intervention from the users themselves.
2Measurement precision
If broad and abstract search terms are used, then users can cast a wide net for team members, but the result is identification of unsuitable team members
Solution Approach 1:
The system uses localized, specific search terms derived from the actual problem description rather than broad abstract terms. By focusing on the specific skills and expertise needed for the particular problem at hand, the system achieves precise identification of suitable team members without requiring complex manual filtering.
Solution Approach 2:
The system dynamically adjusts search parameters based on the specific problem description and user preferences. Instead of using fixed broad search terms, the platform transforms the problem description into specific skill requirements and automatically adjusts the search criteria to match the precise needs of each problem-solver.
3Productivity
If tickets are transmitted throughout a large network of users, then more potential problem solvers can be reached, but it is difficult to provide the ticket to the appropriate team
Solution Approach 1:
The system performs preliminary matching and team assembly before the problem ticket is fully processed. By pre-assembling teams based on problem descriptions and user profiles, the system ensures that when tickets are transmitted, they are already directed to appropriately matched teams rather than being broadcast broadly across the network.
Solution Approach 2:
The platform uses feedback loops to continuously improve matching accuracy. By analyzing successful team formations and problem resolutions, the system refines its matching algorithms to better connect problems with suitable teams, reducing information loss in the matching process while maintaining high productivity.
Data Source
AI summary
Aspects of the present disclosure include a system that automatically defines and assembles a group of users capable of solving a particular problem, such as problems associated with network platforms and infrastructures. The group of users are identified from user data captured while users interact within and throughout a network, such as a social network.