AI Project Workflow Evaluation for Multi-Actor Delay Prediction

The patent divides complex projects into smaller, manageable tasks and assigns them to different actors. Each task can be monitored independently, reducing the complexity of overall project tracking while maintaining accuracy through granular progress measurement at the task level rather than requiring continuous monitoring of the entire project by multiple managers.

The system enables automated progress tracking where actors update their own task completion status based on predefined parameters. This self-service mechanism eliminates the need for continuous manual monitoring by multiple managers, reducing management complexity while maintaining tracking accuracy through systematic data collection from task performers.

The patent implements an automated feedback mechanism that collects progress data from multiple actors performing tasks. The system aggregates completion percentages from individual actors and feeds this information back to calculate overall project progress automatically. This eliminates the manual tracking difficulty while maintaining high productivity through efficient multi-actor task execution.

The system creates a universal progress tracking mechanism that works across diverse task types and actor combinations. A single standardized interface and calculation method handle progress aggregation regardless of the number of actors or task complexity, making tracking equally easy for simple or complex project structures while maintaining productivity benefits of task division.

The patent transforms multiple actor-specific parameters into a standardized completion percentage metric through systematic parameter transformation. Each actor's progress data is converted to a common scale and weighted according to their contribution, then aggregated into an overall completion percentage. This parameter transformation approach maintains measurement accuracy while reducing calculation complexity by establishing consistent conversion rules.

The system discards raw, complex actor-specific progress data and recovers it in the form of simplified completion percentages. Individual actor parameters with varying units and scales are temporarily discarded in favor of normalized values, then recovered as meaningful completion metrics through weighted aggregation. This discarding and recovering process simplifies calculations while preserving measurement accuracy.