Probabilistic Process Model for Semi-Structured Task Prioritization
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Solution Overview
Problem
Current methods for task scheduling in semi-structured business processes, such as case-oriented processes, lack a formal process model to effectively predict and optimize task execution, leading to inefficiencies and suboptimal decision-making due to non-deterministic human-driven processes.
Innovation Solution
A computer-implemented method for predictive analytics using a probabilistic process model that updates task probabilities based on completed tasks, prioritizes tasks by cost, and recommends the next task to execute, incorporating document content and decision trees to adapt to changing process conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a formal process model is implemented to drive semi-structured processes, then predictability and optimization of task execution improve, but the complexity of the system increases
Solution Approach 1:
The patent replaces complex mechanical scheduling systems with a probabilistic model that uses statistical patterns and machine learning algorithms to predict task execution outcomes, eliminating the need for rigid formal process models while improving predictability
Solution Approach 2:
The system dynamically adjusts probability parameters based on historical data and completed tasks, allowing the model to adapt to changing process conditions without requiring a fixed formal process model, thus improving reliability while maintaining flexibility
2Productivity
If task scheduling optimization is applied to minimize penalties, then task completion efficiency improves, but the computational complexity increases
Solution Approach 1:
The system automatically learns optimal task scheduling strategies from historical data and completed tasks, enabling self-optimization without requiring complex external optimization algorithms or manual intervention, thus improving productivity while reducing computational burden
Solution Approach 2:
The patent implements a feedback mechanism where completed tasks update the probabilistic model parameters, allowing the system to continuously improve its scheduling decisions based on actual outcomes, achieving optimization through iterative learning rather than complex computational methods
3Adaptability or versatility
If human decision making drives semi-structured processes, then flexibility and adaptability improve, but determinism and predictability deteriorate
Solution Approach 1:
The system dynamically adjusts probability predictions based on real-time data and completed tasks, allowing the model to adapt to changing human decision-making patterns while providing statistically reliable predictions about task execution outcomes, thus reconciling flexibility with predictability
Data Source
AI summary
A method for predictive analytics in a semi-structured process including updating, iteratively, at least one probability of a probabilistic process model based on a completed task, wherein updating the at least one probability of the probabilistic process model includes receiving the probabilistic process model associated with a todo list including a plurality of tasks of the semi-structured process, defining a cost of each of the plurality of tasks, prioritizing the plurality of tasks according to the costs, and recommending a next task from the todo list according to a prioritization