Bootstrap Scheduling Using Historical Time-Sequence Matching
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Solution Overview
Problem
Existing scheduling systems lack efficient methods for networked, autonomous resource utilization and forecasting of employee schedules, particularly in handling absenteeism and employee availability, while balancing employer and employee interests.
Innovation Solution
The system employs agglomerate networks with scheduling factor interpretation, agglomerate network circuits, connector circuits, and schedule provisioning circuits to generate and optimize schedules, incorporating feedback loops and historical data for dynamic adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional scheduling methods are used, then implementation is simple, but scheduling efficiency and adaptability are poor
Solution Approach 1:
The patent replaces traditional mechanical scheduling systems with an agglomerate neural network-based intelligent system. The scheduling factor interpretation circuit uses machine learning models to automatically interpret scheduling factors and generate optimized schedules, substituting manual or rule-based scheduling mechanisms with adaptive computational intelligence that learns from historical data and dynamically adjusts to changing conditions.
Solution Approach 2:
The scheduling system performs self-optimization through the agglomerate network's ability to automatically learn from historical scheduling data and performance feedback. The system continuously refines its scheduling predictions and adjustments without external intervention, using embedded learning mechanisms that adapt to organizational patterns, employee preferences, and operational requirements autonomously.
2Productivity
If schedules are optimized for employer interests, then operational efficiency improves, but employee satisfaction deteriorates
Solution Approach 1:
The system dynamically adjusts scheduling parameters by introducing weighing mechanisms that balance employer and employee interests. The scheduling factor interpretation circuit can modify the importance weights of different scheduling factors (e.g., operational coverage vs. employee preferences) based on organizational goals and contextual conditions, enabling flexible optimization that adapts to different priorities without sacrificing either efficiency or satisfaction.
Solution Approach 2:
The patent implements dynamic scheduling optimization where the system can shift its optimization focus between employer and employee interests based on changing conditions. The agglomerate network continuously learns from feedback and adjusts its prediction models to balance competing objectives, transforming static scheduling rules into dynamic decision-making that responds to real-time organizational needs and employee welfare considerations.
3Manufacturing precision
If multiple scheduling factors are considered, then schedule quality improves, but processing complexity increases
Solution Approach 1:
The patent combines multiple scheduling factor interpretation functions into a unified agglomerate neural network architecture. Instead of processing each scheduling factor (historical data, employee preferences, operational requirements, etc.) through separate complex algorithms, the system integrates them into a single learned model that simultaneously processes all factors and generates optimized schedules, reducing overall processing complexity while maintaining high schedule quality.
4Measurement precision
If historical data is extensively used for predictions, then prediction accuracy improves, but data privacy concerns increase
Solution Approach 1:
The system introduces an intermediary processing layer where historical data is transformed and aggregated into anonymized patterns before being used for predictions. The scheduling factor interpretation circuit processes individual employee data through the agglomerate network, which learns from aggregated historical patterns rather than raw individual records, maintaining prediction accuracy while protecting employee privacy through intermediate data transformation and aggregation mechanisms.
Data Source
AI summary
In embodiments, an apparatus includes a user data interpretation circuit structured to interpret user data corresponding to a first user; a bootstrap circuit structured to: match the first user to a second user via querying one or more databases based at least in part on the user data; retrieve historical time sequence data associated with the second user via querying the one or more databases; extract a time sequence trend from the historical time sequence data; identify a portion of the historical time sequence data corresponding to the extracted time sequence trend; and generate, based at least in part on the identified portion, time sequence data corresponding to the first user; and a time sequence data provisioning circuit structured to transmit the time sequence data.