Transaction Error Root Cause Analysis via Frequency Sampling
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Solution Overview
Problem
Current communication network protocols, such as HTTP/1 and HTTP/2, provide insufficient information to efficiently determine the root causes of transaction failures due to their complex and extensible nature, requiring substantial human resources and becoming increasingly challenging with growing transaction volumes.
Innovation Solution
A method that involves sampling transaction packets to determine the occurrence frequencies of data fields and values in successful and failed transactions, identifying statistically significant differences to pinpoint marker fields and values that indicate root causes of failures, thereby automating the identification of error causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If transaction messages use complex and extensible protocols (HTTP/1, HTTP/2) with fully extensible headers and ad-hoc data fields, then adaptability and versatility of communication are improved, but difficulty of detecting and measuring root causes of errors increases
Solution Approach 1:
The patent introduces an intermediary system that sits between the complex transaction protocols and the error analysis process. This intermediary automatically captures, standardizes, and structures error information from extensible protocols into a consistent format, making root cause analysis feasible without changing the underlying protocol flexibility
Solution Approach 2:
The patent replaces manual human analysis of complex transaction messages with an automated computational system. The system uses algorithms to parse, compare, and identify root causes of errors in transaction packets, substituting mechanical human effort with automated information processing
2Measurement precision
If manual methods are used to identify root causes of transaction failures, then measurement precision may be maintained, but productivity and loss of time increase substantially
Solution Approach 1:
The patent implements a self-service system where the error analysis tool automatically performs root cause identification without requiring human intervention. The system captures transaction packets, analyzes them autonomously, and generates root cause reports, enabling continuous operation at high throughput while maintaining precision through consistent algorithmic analysis
Solution Approach 2:
The patent performs preliminary capture and standardization of error information as transaction packets arrive, preparing the data in advance for rapid analysis. This preliminary processing of transaction packets includes structuring the data and identifying potential error patterns before full root cause analysis is initiated, enabling faster overall processing
3Measurement precision
If comprehensive error information is captured from all transaction packets, then measurement precision is improved, but device complexity and use of energy increase
Solution Approach 1:
The patent extracts only the relevant error information from comprehensive transaction packets using predefined capture rules. Instead of processing all data fields in every packet, the system selectively extracts error-related fields based on transaction type, protocol, and error conditions, reducing processing complexity while maintaining precision for error analysis
Solution Approach 2:
The patent segments the error analysis process into distinct modules: packet capture, data standardization, error identification, and root cause determination. Each module handles specific aspects of the analysis, reducing overall system complexity by breaking down the comprehensive error analysis task into manageable, specialized components
Data Source
AI summary
A method of identifying anomalies characterizing failed transactions engaged in via a communications network, the method comprising: determining occurrence frequencies of fields and/or field values exhibited by the fields in successful transaction messages engaged in via the network; determining occurrence frequencies of fields and/or field values exhibited by the fields in messages of failed transactions engaged in via the network; and processing the occurrence frequencies to determine anomalous occurrence frequencies exhibited by the failed transactions.


