Error Severity Module for E-Commerce Revenue Loss
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Solution Overview
Problem
Current methods for evaluating errors in computer systems, particularly e-commerce applications, are inefficient due to the time-consuming nature of parsing output logs, lack of context, and difficulty in prioritizing errors based on their impact on users, leading to incomplete error reporting and inefficient resource allocation for correction.
Innovation Solution
A system and method that utilize an error-severity module to rank errors based on their severity, incorporating factors like frequency and user impact, and generate user interfaces showing estimated revenue loss and priority for correction, leveraging machine learning and session data to identify and predict errors across multiple systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If developers manually parse output logs to identify errors, then they can detect errors, but the process becomes extremely time-consuming and inefficient
Solution Approach 1:
The system implements automated error detection and severity assessment that operates without manual intervention. The error-severity module automatically parses logs, identifies errors, determines their severity levels, and prioritizes them for correction, eliminating the need for developers to manually analyze output logs while maintaining comprehensive error detection
Solution Approach 2:
The patent replaces the manual mechanical process of log parsing with an automated computational system. The error-severity module uses algorithmic processing to automatically extract, analyze, and prioritize errors from system logs, substituting human developers' manual efforts with an efficient automated mechanism
2Reliability
If all reported errors are treated as critical, then no important errors are missed, but developers waste time on less-critical fixes
Solution Approach 1:
The system applies different quality levels of response to different errors based on their severity. The error-severity module categorizes errors into distinct severity levels (critical, high, medium, low) and provides prioritized guidance for correction, allowing developers to focus their efforts on the most impactful errors while maintaining awareness of all errors
Solution Approach 2:
The patent changes the parameter of error prioritization by introducing a severity assessment mechanism. Instead of treating all errors uniformly, the system dynamically assigns severity levels and priority scores to errors based on their impact on system functionality and user experience, enabling differentiated response strategies
3Loss of information
If users report all errors they encounter, then complete error data is collected, but the volume of data becomes challenging to parse and analyze
Solution Approach 1:
The error-severity module extracts only the most relevant and critical information from the large volume of user-reported errors. It automatically filters, aggregates, and prioritizes error data, extracting key patterns and severity indicators while discarding redundant information, thereby simplifying the data processing burden
4Manufacturing precision
If developers focus on fixing all errors equally, then comprehensive bug fixes are achieved, but resource allocation becomes inefficient
Solution Approach 1:
The system performs preliminary assessment and prioritization of errors before developers begin correction work. The error-severity module pre-evaluates all reported errors, assigns severity levels, and creates a prioritized correction queue, allowing developers to efficiently allocate their resources to the most critical errors first while ensuring comprehensive coverage over time
Data Source
AI summary
Systems and methods for tracking and ranking errors in computer systems may be used in e-commerce applications in order to identify errors that occur in e-commerce user sessions along with an estimate of potential lost revenues resulting from the error. The errors and associated lost revenues may allow prioritizing of which errors to address.