Consensus-Based Remediation System for Automated Issue Resolution
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Solution Overview
Problem
Users and administrators face challenges in identifying and implementing effective remediations for product or service issues due to the lack of timely and efficient solutions, often requiring assistance from expensive subject matter experts.
Innovation Solution
A consensus-based remediation system that identifies possible remediations from online sources, determines their efficacy, and outputs the most effective solutions, allowing users to apply them without expert intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users and administrators manually identify and implement remediations for product or service issues, then they can potentially find solutions, but the process is time-consuming and requires expensive expert assistance
Solution Approach 1:
The system enables users to independently identify and implement remediations by automatically analyzing error messages, searching online sources for solutions, and presenting ranked remediation options. This self-service approach eliminates the need for expensive expert assistance while reducing resolution time through automated information gathering and filtering.
Solution Approach 2:
The system acts as an intermediary between users and the vast amount of online remediation information. It automatically queries multiple online sources, filters relevant solutions, and presents them in a structured format, bridging the gap between raw information and actionable remediation steps for users.
2Adaptability or versatility
If users search online sources for remediation solutions, then they can find possible solutions, but the information is unstructured and difficult to evaluate for effectiveness
Solution Approach 1:
The system incorporates feedback mechanisms by analyzing successful remediation outcomes from online sources and using this information to rank and prioritize remediation options. This feedback loop enables the system to learn from past successes and present the most effective solutions first, making it easier for users to select appropriate remediations.
Solution Approach 2:
The system transforms unstructured online information into structured, evaluable data by applying multiple evaluation criteria (relevance, success rate, recency, authority). This parameter-based transformation converts chaotic information into a ranked list of remediation options with measurable attributes, enabling easy comparison and selection.
3Productivity
If multiple online sources are searched for remediation information, then more possible solutions can be found, but the information becomes overwhelming and unstructured
Solution Approach 1:
The system segments the overwhelming information from multiple online sources into distinct, manageable components by evaluating each source independently against multiple criteria. It divides the information processing task into separate evaluation dimensions (relevance, success rate, recency, authority) and synthesizes results into a structured ranked list, making complex information tractable.
Solution Approach 2:
The system employs a universal evaluation framework that works across diverse online sources and problem types. The same multi-criteria evaluation methodology is applied regardless of the source or nature of the error, providing a consistent approach to filtering and ranking remediation information from any online source.
Data Source
AI summary
Consensus-based remediation of offerings' problems is described. A system can receive an indication of an offering's problem. The system identifies possible remediations for the offering's problem from on-line sources of remediations. The system determines an efficacy for each possible remediation. The system arranges each possible remediation in order based on its efficacy. The system outputs the possible remediations based on their order. The system can store the ordered possible remediations into a repository.