Predictive Analysis Engine for Support Ticket Severity

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Large business entities face difficulties in managing and resolving a high volume of support tickets efficiently, leading to potential fines or liability due to unresolved issues within specified time frames, which can result in customer escalation and legal consequences.

Innovation Solution

A computer-implemented method that utilizes a predictive analysis engine to generate a severity index score for active support tickets, measuring the likelihood of outcomes such as missed Service Level Agreements (SLA), customer churn, or lawsuits, by applying correlation factors and predictive algorithms, allowing for proactive resource allocation and issue management.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If the number of support tickets increases to handle more customer issues, then customer coverage improves, but the difficulty of managing and resolving tickets increases leading to potential fines and liability

Engineering Contradiction:

Improvenumber of support ticketsVSAvoiddifficulty of managing tickets

Core Design Contradiction:

Quantity of substanceVSDevice complexity

Solution Approach 1:

The patent introduces a predictive analysis engine as an intermediary system between support ticket creation and resolution. This engine automatically analyzes ticket attributes, applies predictive algorithms, and generates severity scores without requiring manual intervention, thereby managing increased ticket volumes without proportionally increasing management complexity

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual ticket management processes with automated computational systems. Predictive algorithms automatically process ticket data, calculate severity scores, and prioritize tickets, substituting human analytical efforts with machine-based systems that can handle larger volumes without increasing operational complexity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If manual review of all support tickets is performed to prevent missed SLAs, then service level agreement compliance improves, but the time and resources required increase significantly

Engineering Contradiction:

ImproveSLA complianceVSAvoidtime for ticket review

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent applies preliminary action by performing predictive analysis on ticket attributes immediately when tickets are created or updated. The system pre-calculates severity scores and identifies high-risk tickets before SLA deadlines approach, enabling proactive resource allocation and reducing the need for time-consuming manual reviews later

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent extracts only the most critical tickets for manual review by filtering and prioritizing based on predicted severity scores. Instead of reviewing all tickets, the system identifies and extracts high-risk tickets that require human attention, significantly reducing the time and resources needed for manual monitoring while maintaining SLA compliance

Inventive Principle:

Principle #2Taking out (Extraction)

3Reliability

If additional resources are allocated to monitor all support tickets, then adverse outcomes like lawsuits and customer churn are reduced, but the cost and complexity of the support system increase

Engineering Contradiction:

Improveprevention of adverse outcomesVSAvoidcomplexity of support system

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent applies local quality by directing additional monitoring resources selectively to high-risk tickets rather than uniformly across all tickets. The predictive analysis engine identifies specific tickets with high probabilities of adverse outcomes and allocates monitoring resources locally to those cases, preventing adverse outcomes without proportionally increasing overall system complexity

Inventive Principle:

Principle #3Local quality

4Measurement precision

If predictive algorithms are applied to all active support tickets, then identification of high-risk tickets improves, but the computational resources and processing time required increase

Engineering Contradiction:

Improveaccuracy of severity predictionVSAvoidcomputational resources

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent applies partial action by running predictive algorithms selectively on tickets that meet certain criteria or in batches, rather than continuously on all active tickets. The system performs predictive analysis on new tickets and updates when significant changes occur, rather than constantly re-evaluating all tickets, reducing computational resource usage while maintaining prediction accuracy for high-risk cases

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS9785918B2Predicting the severity of an active support ticket

Publication Date: 2017.10.10 SAP SE

AI summary

An issue tracking system capable of predicting the likelihood that an outcome of interest will occur during the lifecycle of an active support ticket. The likelihood can be represented as a severity index score. The issue tracking system can apply a predictive algorithm on attributes of the active support ticket to generate the severity index score. The predictive algorithm to use can depend on the outcome of interest while the correlation factors used to configure the predictive algorithm can depend on support tickets that have already been completed.