Automated Error Trend Detection for Communication Anomalies
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Solution Overview
Problem
Current machine learning and alert provision systems lack efficient methods for automatically resolving computerized electronic communication anomalies, particularly in identifying trends in error messages and providing effective error solutions to users.
Innovation Solution
A system that includes a server capable of receiving error messages, comparing them to gathered error data, and providing error solutions by identifying trends, which can generate and send alerts to user devices to launch applications displaying error data, incorporating user profile, hardware, and software information for personalized error remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual error analysis and resolution methods are used, then error solutions can be provided, but the system lacks efficiency and automation in identifying trends and providing timely error solutions
Solution Approach 1:
The system enables automatic self-service error resolution by collecting error messages from multiple user devices, analyzing them through machine learning to identify trends, and automatically generating and pushing error solutions back to users without manual intervention. The server autonomously performs error detection, analysis, and resolution distribution.
Solution Approach 2:
The system implements feedback loops where error messages from users are continuously collected and analyzed, and the resulting error solutions are pushed back to users. This creates a closed-loop system where error data feeds into automated analysis, which generates solutions that are distributed back, improving error resolution efficiency over time.
2Measurement precision
If comprehensive error data collection from multiple users is implemented, then trend identification improves, but system complexity increases
Solution Approach 1:
The server performs multiple functions: collecting error messages from various user devices, storing error data in databases, analyzing error patterns through machine learning, generating error solutions, and distributing them back to users. This multi-functional approach consolidates complexity into a single centralized system rather than requiring complex distributed processing across multiple devices.
Data Source
AI summary
Methods and systems for automatically resolving computerized electronic communication anomalies are disclosed herein. The system can include a memory including an error database containing information identifying a plurality of previous detected errors and configuration information associated with those errors. The system can include a plurality of user devices. Each of these plurality of user devices can include: a first network interface to exchange data via the communication network; and a first I/O subsystem to convert electrical signals to user interpretable outputs via a user interface. The system can include a server that can: receive an indication of the initiation of electronic communication; receive an electrical signal including attribute information; receive an error message; identify a trend in error messages; and provide an error solution if a trend is identified.