Machine Learning Model Candidate Selection
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Solution Overview
Problem
Conventional techniques for improving organizational performance are inefficient and costly, relying on inflexible, expensive hardware and software that often generate inaccurate results due to manual intervention and rules-based logic, failing to effectively utilize large amounts of data for identifying optimal solutions.
Innovation Solution
A system utilizing machine and deep learning processes models performance and behavioral data to develop predictive models that identify suitable candidates by processing raw performance data against behavioral and survey data, leveraging customized templates and training datasets to generate accurate and efficient results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional rules-based logic and manual intervention are used to process data, then implementation is simpler, but processing speed and accuracy deteriorate
Solution Approach 1:
The patent replaces manual human operators with automated machine learning models that process data without human intervention. The system automatically trains models on historical performance data and applies them to evaluate new candidates, eliminating the need for manual review while significantly improving processing speed and consistency.
Solution Approach 2:
The system performs self-service by automatically training machine learning models on organizational performance data and using these models to independently evaluate candidates. The models continuously learn from new data without requiring manual retraining or human intervention, enabling autonomous decision-making that scales efficiently.
2Adaptability or versatility
If manual intervention and human operators are used to adjust criteria, then flexibility is improved, but time consumption and cost increase
Solution Approach 1:
The patent implements dynamic adaptability by allowing the machine learning models to automatically adjust their evaluation criteria based on newly input organizational data. When organizations provide new performance metrics or organizational context, the models automatically retrain and adapt their selection criteria without requiring manual reconfiguration, maintaining flexibility while eliminating time-consuming manual adjustments.
3Device complexity
If conventional software and hardware are used to parse large data sets, then system requirements are lower, but solution accuracy and effectiveness deteriorate
Solution Approach 1:
The patent changes the fundamental parameters of data processing by transitioning from conventional rule-based algorithms to machine learning models that automatically learn patterns from data. This parameter change enables the system to achieve high measurement precision in data analysis accuracy while maintaining reasonable computational requirements through efficient model architectures and progressive training approaches.
4Ease of manufacture
If rules-based logic is applied to data problems, then implementation is more straightforward, but solution effectiveness and success rates deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where machine learning models continuously learn from actual organizational performance data and adjust their predictions accordingly. The system incorporates feedback loops that allow models to refine their accuracy by analyzing the outcomes of their recommendations against actual results, thereby improving solution effectiveness and success rates over time while maintaining ease of implementation through automated feedback processing.
Data Source
AI summary
Techniques for machine and deep learning process modeling of performance and behavioral data are described, including receiving objective performance data, including an induction document, and raw performance data, validating the raw performance data, determining whether incumbent data is sufficient to build a model, building a model to generate an output performance dataset, evaluating a behavioral dataset generated using behavioral attributes determined from a survey, identifying a candidate file using the model, the model being identified as a model candidate, and evaluating the model candidate against one or more other model candidates using one or more exit criteria to determine whether the model candidate, relative to the one or more other model candidates, is used to identify a release candidate.