Goal Standardization via ML Classification
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Solution Overview
Problem
In large organizations, the process of performance appraisal faces challenges in completeness and correctness of role definition due to a high number of manually created goals that do not fit standard templates, leading to difficulties in comparing employee expectations and assigning appropriate roles.
Innovation Solution
A processor-implemented method and system for goal standardization that identifies labeled and unlabeled goals, trains classifiers using goal descriptions and self-comments, classifies unlabeled goals, determines confidence scores, and iteratively co-trains classifiers to assign class labels and cluster manually created goals, thereby standardizing goals and improving role definition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If supervisors manually create goals for employees, then goals can be customized to fit specific situations, but the completeness and correctness of role definition deteriorates due to high number of non-template goals
Solution Approach 1:
The patent introduces an automated classification system as an intermediary between manually created goals and template goals. The system uses machine learning classifiers to automatically categorize manually created goals into appropriate template goal categories, reducing the burden on supervisors while maintaining role definition accuracy. This intermediary process enables both customization and standardization to coexist.
Solution Approach 2:
The patent creates copies of template goals and uses them as training data for classification algorithms. By copying existing template goal structures and using them to train the system, the patent enables automatic classification of new manually created goals without requiring complete manual review, thus maintaining both adaptability and precision.
2Adaptability or versatility
If a high number of manually created goals are assigned, then employee-specific needs can be addressed, but productivity deteriorates due to increased review and role definition checks
Solution Approach 1:
The patent implements a self-service classification system where the performance appraisal system automatically categorizes and processes manually created goals without requiring extensive human intervention. The machine learning model serves itself by continuously learning from labeled goals and automatically classifying new goals, thereby maintaining employee-specific customization while improving processing efficiency.
Solution Approach 2:
The patent performs preliminary classification of manually created goals using trained classifiers before final review. By pre-processing and categorizing goals in advance, the system reduces the time required for subsequent review and role definition verification, thus improving overall productivity while maintaining adaptability.
3Manufacturing precision
If standard goal templates are used for all employees, then role definition completeness is improved, but adaptability deteriorates as goals cannot fit specific situations
Solution Approach 1:
The patent segments the goal-setting process into two parts: template-based goals for standard roles and manually created goals for specific situations. The classification system then segments manually created goals into appropriate template categories, allowing both standardized and customized goals to coexist within a unified framework, thus maintaining both completeness and flexibility.
Solution Approach 2:
The patent creates a universal classification system that can handle both template goals and manually created goals through a single automated process. This multi-functional system categorizes goals regardless of their origin, enabling the organization to maintain complete role definitions while accommodating flexible, situation-specific goal setting.
4Measurement precision
If incorrect role assignments are made, then employee expectations cannot be properly compared, but the number of manually created goals increases as supervisors try to compensate
Solution Approach 1:
The patent implements a feedback mechanism where the classification system continuously learns from labeled goals and improves its accuracy over time. By providing feedback loops where classified goals are reviewed and relabeled when necessary, the system improves role assignment accuracy, which in turn reduces the need for compensatory manually created goals.
Solution Approach 2:
The patent replaces the mechanical process of manual role assignment and goal creation with an automated machine learning-based classification system. This substitution improves measurement precision by consistently applying classification rules, thereby reducing incorrect role assignments and the subsequent need for numerous manual adjustments.
Data Source
AI summary
This disclosure relates generally to performance appraisal management, and more particularly to standardization of goals associated with performance appraisal. In one embodiment, a method for standardization of goals includes identifying labeled and unlabeled goals associated with a role. The goals includes template and manually created goals. Each of the template goals is associated with a class label, and includes corresponding goal description and self-comments. First and second classifiers are trained using goal description and self-comments. Candidate negative goals are identified and excluded from the goals to obtain a set of unlabeled goals. The set of unlabeled goals are classified by the first and second classifier, and a confidence score associated with the classification is determined. The unlabeled goals with high confidence score are added to labeled goals to obtain an updated set of labeled goals. The first and second classifiers are iteratively co-trained using the updated set of labeled goals.