Machine Learning Online Action Recommendations for Career Progression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Employees lack a holistic and accurate view of their career progression paths, leading to unpredictable decisions and biased recommendations based on human judgment.
Innovation Solution
A computerized system utilizing machine learning to analyze employee profile data, cluster users with similar attributes, and apply linear regression to provide dynamic, automated recommendations for career advancement through online actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human judgment is used to provide career recommendations, then personal guidance and context understanding are improved, but bias and unpredictability increase
Solution Approach 1:
The patent replaces the mechanical system of human judgment with an automated machine learning system that processes career profile data. The ML model objectively analyzes historical progression data and recommends online actions without human bias, while maintaining consistency through algorithmic decision-making based on learned patterns from training data.
Solution Approach 2:
The system enables employees to receive automated career recommendations without requiring human intervention from managers or coaches. The machine learning model independently processes user profile attributes and historical data to generate personalized recommendations, making the recommendation process self-service oriented and eliminating human bias.
2Adaptability or versatility
If manual processes are used for career recommendations, then personalization is improved, but scalability and efficiency deteriorate
Solution Approach 1:
The patent replaces manual recommendation processes with an automated machine learning system that can process numerous employee profiles simultaneously. The system maintains personalization by analyzing individual profile attributes while achieving scalability through automated computation, eliminating the trade-off between personalization and efficiency.
Solution Approach 2:
The system changes the parameters of the recommendation process from manual human evaluation to automated ML-based analysis. By transforming the input parameters (profile attributes, historical data) through learned mathematical models, the system achieves both personalization through individualized analysis and high productivity through automated processing of multiple users concurrently.
3Measurement precision
If comprehensive career data is collected and analyzed, then recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex career recommendation system into distinct functional modules: data collection module, machine learning model training module, recommendation generation module, and user interface module. Each module handles specific tasks independently, reducing overall system complexity while enabling comprehensive data analysis for accurate recommendations.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw career data and recommendation outputs. The ML model serves as a mediator that automatically processes complex relationships in career progression data, transforming comprehensive input data into actionable recommendations without requiring complex rule-based systems or manual analysis frameworks.
Data Source
AI summary
The present disclosure generally relates to a computer device, method and system utilizing machine learning for capturing and analyzing profile data communicated across a computing environment including but not limited to: each user's profile, online behaviors and career progression path and provides dynamic recommendations of online actions to be performed to reach a desired target state.