SVM Worker Ranking for Task Assignment
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Solution Overview
Problem
In complex organizational environments, the departure of an expert leads to workflow interruptions due to the reliance on human expertise for task assignment, and existing skill models are difficult to maintain and update, especially when skills are subjective and private, making it challenging to accurately match worker skills with task requirements.
Innovation Solution
A method using a Support Vector Machine (SVM) to rank workers based on historical task data, generating feature vectors and weight vectors to determine worker suitability for incoming tasks without relying on explicit skill models, utilizing Machine-Learned Ranking (MLR) and Ranking SVM algorithms to optimize worker assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If explicit skill models are used for worker assignment, then task-worker matching can be performed, but the models are difficult to maintain and update when skills are subjective and private
Solution Approach 1:
The patent replaces the manual skill model maintenance mechanism with a machine learning-based automatic learning mechanism. Instead of manually creating and updating skill models, the system uses historical task completion data to automatically train classification models that learn worker skills and task requirements, eliminating the need for manual model maintenance while improving matching accuracy
Solution Approach 2:
The system enables self-service by allowing the machine learning model to automatically learn and update skill profiles from historical data without human intervention. The model continuously improves its understanding of worker capabilities and task requirements by processing completed task records, making the skill model maintenance self-sustaining
2Reliability
If human expertise is relied upon for task assignment, then judgments can be made about worker suitability, but workflow interruptions occur when experts depart
Solution Approach 1:
The patent creates digital copies of expert knowledge by training machine learning models on historical task assignment data. These models capture the decision-making patterns and judgment criteria that experts used, preserving institutional knowledge in a form that does not depend on individual experts being present, thus preventing workflow interruptions when experts depart
Solution Approach 2:
The system performs preliminary action by pre-training classification models on historical data before actual task assignments are needed. This allows the system to have ready-made, pre-processed knowledge bases that can immediately handle new task assignments without waiting for expert availability, eliminating workflow delays
3Productivity
If Machine-Learned Ranking is used to dynamically rank workers, then resource utilization efficiency improves, but computational processing is required
Solution Approach 1:
The patent changes parameters by using efficient feature extraction techniques that transform raw task and worker data into compact feature vectors. This reduces the dimensionality and complexity of input data for the ranking model, decreasing computational energy requirements while maintaining the ability to dynamically rank workers based on their suitability for tasks
Data Source
AI summary
A method of ranking workers for an incoming task includes recording a list of completed tasks in a computer data structure, extracting first attributes from the list for the tasks that were completed during a pre-determined period, generating a first feature vector for each task and worker from the first extracted attributes, training a Support Vector Machine (SVM) based on the feature vector to output a weight vector, extracting second attributes from an incoming task, generating a second feature vector for each worker based on the second extracted attributes, and ranking the workers using the second feature vectors and the weight vector. The first attributes may be updated during a subsequent period to re-train the SVM on updated first feature vectors to generate an updated weight vector. The workers may be re-ranked based on the second feature vectors and the updated weight vector. Accordingly, the feature vectors are dynamic.