Neural Network Workforce Recommendation Platform
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Solution Overview
Problem
Human resources personnel face challenges in determining optimal workforce types for new or existing occupational roles within organizations, as existing occupational databases often provide limited guidance, leading to subjective and inefficient decision-making processes.
Innovation Solution
A workforce decision platform that processes occupational activity descriptions and role attributes to generate estimated attribute values, trains neural network models, and utilizes logistic regression to provide probabilistic recommendations for suitable workforce types, automating the determination of optimal workforce types for new roles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human resources personnel manually determine workforce types using existing occupational databases, then decision-making flexibility is maintained, but the process becomes subjective and inefficient
Solution Approach 1:
The patent replaces the manual mechanical decision-making process with an automated machine learning system. The system uses trained models to process occupational activity descriptions and automatically generate workforce type recommendations, eliminating human subjectivity while improving efficiency. The machine learning models process structured and unstructured data to provide objective, data-driven recommendations.
2Measurement precision
If automated machine learning models are used to determine workforce types, then objectivity and efficiency improve, but computational resources and model complexity increase
Solution Approach 1:
The patent divides the complex determination process into distinct modular components: data processing module, feature extraction module, model training module, and recommendation generation module. Each module handles specific tasks independently, making the overall system more manageable and maintainable while preserving automation benefits.
Solution Approach 2:
The patent introduces structured occupational activity descriptions as an intermediary representation between raw occupational data and final workforce type recommendations. This intermediary structure standardizes input data, making it easier for machine learning models to process while reducing the complexity of direct processing from raw data to recommendations.
3Measurement precision
If comprehensive occupational data is processed to improve recommendation accuracy, then measurement precision improves, but processing time and computational cost increase
Solution Approach 1:
The patent performs preliminary processing of occupational activity descriptions during an offline training phase. The machine learning models are trained in advance on comprehensive datasets, allowing the system to quickly generate recommendations during online operation without reprocessing all raw data each time. This separates the computationally intensive training phase from the faster inference phase.
Solution Approach 2:
The patent extracts and processes only the most relevant features from occupational activity descriptions based on their importance to workforce type determination. Rather than processing all data uniformly, the system identifies and focuses on key attributes and activities that have the greatest impact on recommendation accuracy, reducing overall processing requirements.
Data Source
AI summary
A device receives occupational activity descriptions and occupational role attributes, and processes the occupational activity descriptions to generate estimated occupational activity attribute values. The device trains a neural network model based on the estimated occupational activity attribute values to generate a trained neural network model, and receives a new activity description for a new role in an organization. The device processes the new activity description, with the trained neural network model, to generate estimated new activity attribute values, and processes the estimated new activity attribute values, with the logistic regression model, to generate probabilities that the new role is suitable for different workforce types. The device determines a workforce recommendation for the new role based on the probabilities that the new role is suitable for the different workforce types.