Predictive Model for Automated Conflict Resolution
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Solution Overview
Problem
Existing conflict resolution processes in transport and logistics are inefficient, leading to inconsistent and inaccurate handling of conflicts, which consume significant time and resources.
Innovation Solution
A method and electronic device that utilize a predictive model to analyze data patterns from multiple systems, predicting conflict outcomes with a confidence score, thereby automating conflict identification and resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual conflict handling processes are used with complex rule sets, then conflicts can be resolved with human judgment, but the process consumes significant time and resources leading to delays and inconsistent results
Solution Approach 1:
The patent replaces manual mechanical conflict handling processes with an automated machine learning-based system. The ML model automatically analyzes conflict data, applies learned patterns, and generates resolutions without human intervention, thereby eliminating time delays and ensuring consistent application of resolution logic across all conflicts.
Solution Approach 2:
The conflict resolution system enables self-service by allowing the ML model to autonomously analyze conflicts and generate resolutions without requiring manual human processing. The system serves itself by continuously learning from resolved conflicts and improving its resolution capabilities, reducing dependency on human resources while maintaining reliability.
2Adaptability or versatility
If complex rule sets with numerous variations are used to resolve conflicts, then comprehensive conflict scenarios can be addressed, but the device complexity and processing requirements increase significantly
Solution Approach 1:
The patent transforms the conflict handling approach by changing from fixed rule-based parameters to dynamic machine learning parameters. The ML model learns patterns from historical conflict data and adapts its decision-making parameters automatically, enabling it to handle diverse conflict variations without requiring explicit rules for each scenario, thus reducing system complexity while maintaining versatility.
Solution Approach 2:
The ML-based conflict resolution system provides universal functionality by using a single model architecture that can handle multiple types of conflicts with various variations. Instead of requiring separate rule sets for different conflict scenarios, the universal ML model learns to generalize across all conflict types, simplifying the system while maintaining adaptability to diverse situations.
3Measurement precision
If manual conflict resolution processes are used, then human judgment can be applied, but accuracy and consistency in conflict outcomes are compromised due to time pressure and resource constraints
Solution Approach 1:
The patent replaces manual human judgment processes with automated machine learning analysis. The ML model consistently applies learned patterns from training data to resolve conflicts accurately without being subject to human time pressure or resource constraints, thereby maintaining high accuracy while significantly improving resolution speed and productivity.
Solution Approach 2:
The system performs preliminary action by pre-training the machine learning model on extensive historical conflict data before actual conflict resolution occurs. This preliminary training enables the model to quickly and accurately resolve new conflicts without requiring time-consuming manual analysis, thus achieving both high accuracy and fast productivity in conflict resolution.
Data Source
AI summary
Disclosed is a method, performed by an electronic device, for conflict process control. The method comprises obtaining a first data set from one or more systems; determining, based on the first data set and a predictive model having one or more parameters, one or more conflict data patterns indicative of a conflict; and predicting, based on the one or more conflict data patterns, a conflict result parameter and a confidence score associated with the conflict result parameter.