AI Skill Matching With Blockchain Contracts for SME Assignment
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Solution Overview
Problem
Organizations face challenges in efficiently sourcing the optimal talent for projects due to a lack of oversight into their holistic talent pool, leading to delayed and inefficient project execution.
Innovation Solution
A platform, language, and database agnostic skill-based contract assignment module utilizing reinforcement learning to match projects with subject matter experts (SMEs) based on weighted criteria, ensuring secure contract generation and management on a blockchain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual talent sourcing methods are used, then organizational control over talent assignment is maintained, but project execution is delayed and inefficient
Solution Approach 1:
The system enables self-service through automated AI-driven talent matching. The machine learning model automatically analyzes project requirements and matches them with suitable talent from the holistic talent pool without requiring manual intervention, thus eliminating time loss while maintaining organizational control through the AI system's autonomous operation.
Solution Approach 2:
The patent replaces manual mechanical talent sourcing processes with an AI-based automated system. The machine learning model substitutes human manual review and selection processes, enabling rapid automated matching of talent to projects based on criteria, thereby significantly improving productivity and reducing time loss.
2Measurement precision
If comprehensive talent pool oversight is implemented, then optimal talent can be sourced, but system complexity increases
Solution Approach 1:
The patent introduces an AI intermediary system that mediates between project requirements and the holistic talent pool. The machine learning model acts as an intelligent mediator that automatically processes complex matching criteria and identifies optimal talent, thereby achieving high measurement precision without requiring equally complex manual oversight processes.
Solution Approach 2:
The system implements feedback mechanisms where the AI model continuously learns from past talent assignments and outcomes. This feedback loop enables the model to improve its matching accuracy over time, achieving high measurement precision while the system complexity is managed through automated learning rather than manual refinement.
3Productivity
If automated AI-based talent assignment is implemented, then productivity increases, but trust and governance requirements increase
Solution Approach 1:
The patent implements feedback mechanisms where the AI model continuously learns from past talent assignments and outcomes. This feedback loop enables the model to improve its matching accuracy over time, achieving high measurement precision while the system complexity is managed through automated learning rather than manual refinement.
Solution Approach 2:
The patent replaces manual mechanical talent sourcing processes with an AI-based automated system. The machine learning model substitutes human manual review and selection processes, enabling rapid automated matching of talent to projects based on criteria, thereby significantly improving productivity and reducing time loss.
Data Source
AI summary
Various methods and processes, apparatuses or systems, and media for enabling skill-based contract assignment for completing a particular project are disclosed. A processor trains a model on a set of known criteria data, a plurality of dimensions data, and volume data; and receives a request, via a user interface, from a user to assign the contract for completing the project by selecting criteria determining data. The model applies a weight to the selected criteria determining data; generates a forced-rank list of subject matter experts (SMEs) with rankings and contact information; and transmits the forced-rank list to the user interface. The processor receives user input from the user, via the user interface, establishing an agreement to select an SME from the forced-rank list; transmits an electronic message to the selected SME to accept the agreement; and sets the agreement into a contract on a blockchain to ensure accuracy and encryption.