Machine Learning Worker Skill Profiling System
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Solution Overview
Problem
Existing worker skill assessment technologies are limited in accuracy, relying on subjective human perception and are prone to errors, leading to inefficient training programs where resources are wasted on workers who do not need training, and those in need are not adequately trained.
Innovation Solution
A computer-implemented system using machine learning to objectively assess worker skills by parameterizing a model with historical activity data, learning worker skill levels through machine learning values, and providing output based on these assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If subjective manager assessment is used for worker skill evaluation, then the assessment process is simple and quick, but the accuracy and objectivity of skill measurement deteriorates
Solution Approach 1:
The patent replaces the mechanical human judgment system with an automated machine learning system that processes worker activity data. The system uses algorithms to objectively evaluate worker skills based on historical performance data, eliminating subjective bias while maintaining assessment efficiency through automated processing.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw worker activity data and skill assessment results. This intermediary processes objective activity metrics through learned relationships to produce accurate skill evaluations, bridging the gap between simple data collection and precise skill measurement.
2Device complexity
If manual skill assessment by managers is used, then implementation complexity is low, but training program effectiveness deteriorates due to inaccurate skill identification
Solution Approach 1:
The patent enables the system to automatically assess worker skills and generate training recommendations without requiring manual intervention from managers. The machine learning model self-trains on historical data and autonomously produces skill assessments and training program recommendations, improving reliability while managing complexity through automation.
Solution Approach 2:
The patent implements feedback loops where the system continuously learns from worker performance data and training outcomes. The machine learning model updates its understanding of skill relationships based on observed performance patterns, progressively improving assessment accuracy and training effectiveness while adapting to changing work patterns.
3Adaptability or versatility
If objective appearing factors are manipulated to support subjective assessments, then manager preference can be accommodated, but the reliability of skill assessment deteriorates
Solution Approach 1:
The patent transforms subjective assessment parameters into objective measurable parameters by using machine learning models that process quantifiable activity data. The system changes the parameter space from subjective ratings to objective metrics such as task completion rates, quality measures, and performance indicators, eliminating manipulation while maintaining assessment flexibility through configurable evaluation criteria.
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: parameterizing with training data one or more model that expresses, for a set of historical activities, a known outcome as a function of a plurality of factors, the plurality of factors including one or more activity factor and a worker factor; solving the one or more model to learn by machine learning values of the worker random effect vector associated to the set of historical activities, wherein the values of the worker random effect vector provide an indication of learned worker skill level for respective workers identified by the worker identifiers; and providing, by the one or more processor, one or more output based on the solving.