Predicting Software Issues via Expert Decision Algorithms

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Solution Overview

Problem

Traditional on-site and telephone-based technical support for software issues are expensive, time-consuming, and inefficient, and human expert support does not scale well to meet demand while maintaining quality.

Innovation Solution

A system and method using a problem resolution manager that analyzes diagnostic data to apply predictive algorithms based on historical expert behavior to identify and resolve software problems, providing solutions directly to users or support technicians, thereby automating and expediting the problem-solving process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If traditional on-site or telephone-based technical support is used, then personalized support for customer problems is provided, but the cost is high and the service is time-consuming for both vendor and customer

Engineering Contradiction:

Improvepersonalized support qualityVSAvoidsupport time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system creates a virtual copy of human expert problem-solving behavior through algorithms that analyze historical expert data. These algorithms replicate the decision-making patterns of experienced support personnel, enabling automated systems to provide personalized support without requiring actual human experts to be present for each interaction.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system enables customers to resolve their own problems by providing them with relevant solutions and diagnostic information automatically. The problem resolution manager analyzes customer-submitted data and presents tailored troubleshooting steps and solutions, allowing customers to fix issues independently without waiting for human expert intervention.

Inventive Principle:

Principle #25Self-service

2Reliability

If human support experts are trained to provide personalized support, then quality service is maintained, but the investment in training time and cost is significant

Engineering Contradiction:

Improveservice qualityVSAvoidtraining time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary analysis and preparation by pre-processing historical expert data and building predictive algorithms before customer problems occur. The problem resolution manager is pre-configured with knowledge bases and diagnostic models that can immediately analyze and respond to customer issues without requiring real-time human expert preparation or training.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces the mechanical process of human expert training and decision-making with automated algorithms and data processing. Instead of investing time in training human experts, the system uses computational algorithms to analyze historical data and generate solutions, substituting human cognitive processes with automated mechanical systems.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Reliability

If human support experts are used to meet increased demand, then service quality is maintained, but the system does not scale well and difficulty increases in meeting capacity needs on short notice

Engineering Contradiction:

Improveservice qualityVSAvoidservice capacity

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system creates a universal problem resolution manager that can handle multiple different types of customer problems across various product lines simultaneously. The automated system performs multiple functions including diagnostic analysis, solution generation, and customer communication, replacing the need for multiple specialized human experts with a single multi-functional automated platform that scales effortlessly.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Productivity

If a knowledge base is made available to resolve repetitive problems, then some automation is achieved, but users may not know about it, how to use it, or have sufficient information to search it

Engineering Contradiction:

Improveproblem resolution efficiencyVSAvoiduser ability to access solutions

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The problem resolution manager acts as an intermediary between the customer and the knowledge base. Instead of requiring customers to directly search and navigate the knowledge base themselves, the automated system receives customer problem data, automatically searches the knowledge base for relevant solutions, and presents the information in an easily digestible format. This intermediary layer eliminates the complexity of direct knowledge base interaction while maintaining access to comprehensive solutions.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS9026856B2Predicting symptoms of run-time problems in view of analysis of expert decision making

Publication Date: 2015.05.05 RED HAT INC

AI summary

Systems, methods, and non-transitory computer-readable storage media for receiving historical data describing behavior of human subject-matter experts, wherein the historical data links customer problems with solutions, receiving a plurality of human-generated algorithms describing patterns for linking customer problems with solutions based on problem-specific diagnostic data, comparing each algorithm of the plurality of algorithms with the historical data to determine respective predictive scores for linking a customer problem type with a particular solution, and ranking at least part of the plurality of algorithms based on the respective similarity scores.