AI Engine Predicts Employee Development Actions
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Solution Overview
Problem
Identifying and improving underperforming employees in a corporate setting is complex due to varying performance evaluation criteria across sub-groups and hierarchical organizations, making it difficult to determine specific areas for improvement and implement effective strategies.
Innovation Solution
A server system using artificial intelligence engines analyzes aggregated employee data to determine unified metrics, predicts next best actions based on distribution curves, and sends recommendations to employees and supervisors to enhance performance, incorporating personalized development plans and adaptive AI retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional performance evaluation methods are used across hierarchical organizations with multiple sub-groups, then each sub-group can maintain its own performance evaluation criteria, but the complexity of determining specific areas for improvement and implementing effective strategies increases significantly
Solution Approach 1:
The patent segments the complex performance evaluation system into multiple layers: (1) sub-group level metrics specific to each department, (2) unified enterprise-level metrics that aggregate across all sub-groups, and (3) AI-based analysis that processes segmented data to identify specific improvement areas. This segmentation allows each sub-group to maintain its own criteria while reducing overall complexity through hierarchical aggregation.
Solution Approach 2:
The patent introduces AI-based analytics as an intermediary layer between sub-group performance data and improvement recommendations. This intermediary automatically processes diverse sub-group metrics, identifies patterns, and generates specific improvement recommendations, thereby reducing the complexity that would otherwise exist in directly analyzing multiple sub-group criteria.
2Adaptability or versatility
If multiple sub-groups use their own performance evaluation criteria, then each sub-group can evaluate performance according to its specific needs, but it becomes difficult to determine unified metrics for organization-wide improvement
Solution Approach 1:
The patent creates unified performance metrics that serve multiple functions simultaneously: they aggregate data from diverse sub-group criteria, enable organization-wide comparisons, and maintain sensitivity to sub-group specificities. These multi-functional metrics achieve both adaptability to sub-groups and precision for unified evaluation through AI-based normalization and weighting.
Solution Approach 2:
The patent transforms diverse sub-group performance parameters into standardized unified metrics through AI-based parameter transformation. The system dynamically adjusts weights, normalizes scales, and reweights metrics based on organizational priorities, thereby converting heterogeneous sub-group criteria into precise, comparable unified measurements without losing sub-group specificity.
3Measurement precision
If manual analysis of employee performance data is performed to identify improvement areas, then detailed assessment can be conducted, but the time and resources required increase significantly
Solution Approach 1:
The patent replaces manual mechanical analysis of performance data with AI-based automated analytics. The system uses machine learning algorithms to process employee performance data, identify patterns, and generate improvement recommendations automatically, thereby maintaining detailed assessment precision while eliminating the time-consuming manual analysis process.
Solution Approach 2:
The patent implements self-service performance analytics where the system automatically processes performance data, identifies improvement areas, and generates recommendations without requiring manual intervention. The AI-based engine continuously monitors performance metrics and autonomously generates improvement strategies, freeing managers from time-consuming analysis while maintaining high assessment detail.
Data Source
AI summary
In some examples, a server determines, based on a portion of aggregated data, a plurality of metrics associated with an employee. The aggregated data includes activities performed by the employee using a computing device. The server determines, based on the plurality of metrics, a unified metric associated with the employee. The server determines a distribution curve based on the unified metric associated with the employee and based on additional unified metrics associated with additional employees. The server determines a location of the employee on the distribution curve. The server predicts, using a plurality of artificial intelligence engines executing on the server and based on the location of the employee on the distribution curve, a next best action for the employee and sends the next best action to the employee and to a supervisor of the employee to improve a future performance of the employee.