E-Problem Solving Board Analytics Engine for Automated Issue Classification
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Solution Overview
Problem
Conventional computer-based systems are inefficient in identifying and tracking problems, proposing solutions, and updating records, often leading to wasted resources due to outdated information, duplicated efforts, poor prioritization, and delays in problem resolution, especially when team members are geographically dispersed.
Innovation Solution
A computer-based system with an analytics engine that classifies problems using metadata, assigns employees based on past performance, and suggests solutions, featuring a user interface for timely notifications and automatic updates, utilizing machine learning and collaborative filtering algorithms for efficient problem management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional computer-based systems are used for problem tracking and management, then basic problem recording is possible, but the systems are inefficient and cause wasted resources due to outdated information, duplicated efforts, poor prioritization, and delays in problem resolution
Solution Approach 1:
The system performs preliminary actions by automatically classifying problems using machine learning algorithms before human intervention, pre-assigning problems to appropriate team members based on historical performance data, and proactively notifying stakeholders. This preliminary automation eliminates manual triage steps and reduces the time from problem identification to active resolution.
Solution Approach 2:
The system implements continuous feedback loops by monitoring problem resolution metrics, team member performance, and system effectiveness. This feedback enables the machine learning models to improve their classification and assignment accuracy over time, while also allowing managers to adjust priorities and resources based on real-time performance data, thereby continuously improving problem resolution efficiency.
2Reliability
If manual updating and tracking is performed in conventional systems, then problem records can be maintained, but outdated information and duplicated efforts occur due to lack of automatic updates
Solution Approach 1:
The system enables self-service by automatically updating problem records with relevant information from multiple sources, autonomously classifying new problems based on their characteristics, and self-correcting outdated information through continuous data ingestion. This automation eliminates manual updating requirements while maintaining high information accuracy, as the system serves itself without human intervention for routine maintenance tasks.
Solution Approach 2:
The patent replaces manual mechanical processes of problem tracking and information updating with automated computational systems. Machine learning algorithms substitute for human analysts in classifying problems, while automated notification systems replace manual communication channels. This substitution eliminates human error and inconsistency while maintaining system reliability across distributed teams.
3Adaptability or versatility
If team members work from different geographic locations, then organizational flexibility is improved, but physical exchange of information becomes difficult and communication efficiency decreases
Solution Approach 1:
The system achieves universality by implementing a centralized digital platform that serves multiple functions: problem tracking, automated classification, team member assignment, real-time notification, and performance monitoring. This single universal system replaces multiple separate communication channels and tools, enabling seamless information exchange across geographically distributed teams while maintaining ease of operation through a unified interface.
4Productivity
If conventional systems require manual input for updating, then system simplicity is maintained, but productivity decreases due to manual effort and slower updates
Solution Approach 1:
The system performs preliminary automated actions by pre-classifying problems using machine learning models before human review, pre-assigning problems to optimal team members based on historical performance, and proactively notifying relevant stakeholders. This preliminary automation significantly increases problem management throughput by eliminating manual triage steps while maintaining quality through structured review processes.
Data Source
AI summary
A system and method for an e-problem solving board is disclosed. Said e-problem solving board allows automated classification and management of one or more problems. In some embodiments, the method uses one or more machine learning algorithms for classifying problems according to their complexity. In other embodiments, the method uses collaborative filtering algorithms for classifying the complexity of the problem. In these embodiments, the method uses collaborative filtering algorithms for assigning employees to problems and providing a set of suggestions to address the one or more problems. In some embodiments, the system provides status reports regarding the one or more problems. In other embodiments, the system allows multiple teams, operating in different geographic locations, to work on a single problem. Further to these embodiments, the system allows users to track and continually update problems.