Dynamic Support Request Prioritization via Machine Learning
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Solution Overview
Problem
Large organizations face challenges in efficiently prioritizing incoming support requests due to varying factors such as severity, importance, and contractual obligations, which existing static rules-based systems struggle to adapt to over time.
Innovation Solution
An automated system using a machine learning model that assigns a priority score to support requests based on current and additional information, incorporating hybrid parameter values and sentiment analysis, allowing for dynamic prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static rules-based systems are used to prioritize support requests, then the system is simple to implement, but it cannot adapt to time-varying factors and sensitive information
Solution Approach 1:
The patent implements a dynamic prioritization system that uses machine learning models to continuously adapt priority scores based on current support request information and additional contextual information. The system updates priority rankings in real-time as new information becomes available, rather than using static rules, thereby achieving adaptability to time-varying factors while managing complexity through automated computational processes.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw support request data and priority determination. This intermediary processes both current support request information and additional information (such as sensitive data not available to support personnel) to generate optimized priority scores, effectively mediating the complexity of multiple information sources while providing clear prioritization outcomes.
2Measurement precision
If multiple factors including sensitive information are considered for prioritization, then prioritization accuracy improves, but information accessibility to support personnel is restricted
Solution Approach 1:
The patent segments information into two distinct categories: current support request information that is accessible to support personnel, and additional information that may contain sensitive data not available to support personnel. The machine learning model processes both segments independently and integrates them to generate priority scores, ensuring that prioritization accuracy is improved through comprehensive data usage while maintaining appropriate information access boundaries.
Solution Approach 2:
The machine learning model serves as an intermediary that can access and process sensitive additional information without requiring support personnel to have direct access to this data. The model translates this inaccessible information into priority score adjustments, thereby improving prioritization accuracy while preserving information security and appropriate access controls.
3Productivity
If manual prioritization by support personnel is used, then flexibility in decision-making is maintained, but processing time and human resource requirements increase
Solution Approach 1:
The patent implements a self-service prioritization system where the machine learning model automatically generates priority scores without requiring manual intervention from support personnel. The system autonomously processes current support request information and additional information, computes priority rankings, and updates ticket queues automatically, thereby dramatically improving processing efficiency and eliminating time loss associated with manual priority assessment.
Solution Approach 2:
The patent replaces the mechanical process of manual priority assessment by support personnel with an automated computational system based on machine learning. This substitution eliminates human time requirements for priority evaluation while maintaining or improving accuracy through systematic processing of multiple information factors, thereby significantly increasing overall ticket processing efficiency.
Data Source
AI summary
Methods, information handling systems and computer readable media are disclosed for determining a priority score for a pending support request document. According to one embodiment, a method includes receiving current support request information from within a pending support request document and accessing current additional information associated with the pending support request document. The method further includes associating a set of parameter values with the pending support request document, wherein the values within the set of parameter values are based on information within one or both of the current support request information or the current additional information. The method continues with determining a priority score corresponding to the set of parameter values, where determining the priority score comprises applying a machine learning model developed using previous support request information and previous additional information associated with previously-resolved support request documents, and assigning the priority score to the pending support request document.