Skill Development Recommendations for Near-Miss Shift Matching
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Solution Overview
Problem
Traditional temporary employment staffing systems lack efficient methods for matching workers to jobs, require significant investment in recruiting, and do not facilitate quick scale-up or scale-down of the workforce, while also failing to encourage personal growth and skill development among workers.
Innovation Solution
A system that provides provisional job matches based on near-miss requirements, generates automated resumes, and gamifies platform participation to encourage skill development and improve worker qualifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional staffing methods are used with branch offices and manual filtering, then workers can be placed in positions, but the process is inefficient and requires significant investment in recruiting and filtering applications
Solution Approach 1:
The patent replaces manual filtering and mechanical recruitment processes with an automated digital platform that uses algorithms to match workers with jobs. The system automatically processes applications, evaluates qualifications, and recommends matches, eliminating the need for manual filtering by recruiters or branch office staff.
Solution Approach 2:
The platform enables workers to self-register, self-evaluate their skills, and self-present for job opportunities without requiring extensive recruiter intervention. Workers can update their profiles, view matching jobs, and apply autonomously, reducing the burden on traditional staffing systems.
2Adaptability or versatility
If traditional staffing systems retain workers for long-term positions, then stability is achieved, but the system cannot enable quick scale-up or scale-down of workforce
Solution Approach 1:
The patent implements a dynamic staffing system where worker-job assignments are flexible and can be quickly adjusted based on demand. The digital platform allows real-time matching and reassignment of workers to different positions, enabling rapid scaling up or down without the constraints of long-term retention agreements.
Solution Approach 2:
The platform creates a universal pool of workers who can be deployed across multiple different job types and positions. Workers develop diverse skills through various assignments and can be rapidly reassigned to different roles, making the workforce highly adaptable to changing business needs while maintaining stability through continuous engagement.
3Productivity
If traditional staffing focuses on immediate placement, then quick filling of positions is achieved, but worker skill development and personal growth are not encouraged
Solution Approach 1:
The system performs preliminary skill assessments and identifies skill gaps before job placement. It proactively recommends training programs and skill development opportunities to workers, preparing them in advance for future job opportunities that require higher qualifications, thus improving both immediate placement and long-term career development.
Solution Approach 2:
The platform implements continuous feedback loops where workers receive performance evaluations, skill assessments, and personalized recommendations for improvement. This feedback mechanism motivates workers to develop additional skills while the system uses this information to make better-matched placements, simultaneously improving productivity and worker qualification.
Data Source
AI summary
Disclosed is a platform that manages worker users in a temporary staffing environment via an artificial machine learning model. The temporary staffing platform matches available workers to available shifts/gigs. Additional features include generating provisional or near-miss matches and informing workers how to turn those near-misses into full matches, plotting a gig-career path to develop additional skills, gamify development, and automatically generate resumes. The platform generates a set of skill tags associated with each shift/gig performed by the user. Designing of resume text files by the artificial machine learning model includes procedurally generated descriptions of experience the user has based on the recording of each shift/gig performed by the user and the skill tags associated with each recorded shift/gig, wherein a format of the resume text file is formulated by the artificial machine learning model evaluating a mix of skill tags and employers amassed by the user.