Analytical RPA Error Parsing for Application Stack Issue Resolution

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Solution Overview

Problem

Robotic process automation (RPA) systems face challenges in efficiently processing and resolving issues within complex application stacks, where diverse applications with different architectures and error formats lead to unstructured data, making it difficult to identify and automate error resolution.

Innovation Solution

An analytical RPA system that uses cognitive intelligence, machine learning, and natural language processing to parse and tokenize error messages, generate unique keys, and match solutions from a knowledge base, enabling automated execution of processor-readable instructions to resolve issues through bots, while updating the knowledge base with user inputs for improved performance over time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If RPA systems process diverse applications with different architectures and error formats, then the system can handle more complex application stacks, but the data becomes unstructured and harder to process

Engineering Contradiction:

Improveability to handle diverse application stacksVSAvoidcomplexity of processing unstructured data

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments unstructured error data into structured components using parsing and tokenization. Error messages are divided into tokens representing different elements (error type, application name, error code, etc.), transforming unstructured text into structured data that can be systematically processed and matched against known error patterns.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediary layer (parser and tokenizer) between the diverse application error outputs and the RPA processing logic. This intermediary standardizes various error formats into a common structured representation, enabling uniform processing without requiring changes to the underlying diverse applications.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If manual methods are used to identify and resolve errors in application stacks, then flexibility in handling unique error cases is maintained, but processing time and efficiency increase significantly

Engineering Contradiction:

Improveerror resolution speedVSAvoidtime required for manual error identification

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-processing error messages through parsing and tokenization, and by maintaining a knowledge base of known error patterns and solutions. When an error occurs, the system can quickly match it against pre-analyzed patterns rather than requiring manual analysis from scratch, significantly reducing resolution time.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms where resolved errors and their solutions are added to the knowledge base. This feedback loop allows the system to learn from past errors and improve future error resolution efficiency, creating a self-improving system that reduces processing time over time.

Inventive Principle:

Principle #23Feedback

3Productivity

If automated bots are used to resolve errors, then processing efficiency improves, but the ability to handle unique and novel error cases requiring human judgment decreases

Engineering Contradiction:

Improveautomated error resolution efficiencyVSAvoidability to handle novel error cases

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system creates a universal error resolution framework that can handle both common and novel error cases. The knowledge base stores patterns for known errors, while the parsing and matching mechanisms can identify and handle new error types by comparing them against stored patterns, providing multi-functional capability that covers both automated routine cases and novel situations.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system implements self-service capabilities where bots automatically resolve errors by matching them against the knowledge base without human intervention for routine cases. The system serves itself by automatically learning from resolved errors and updating its knowledge base, reducing the need for human involvement while maintaining adaptability.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS10960541B2Analytical robotic process automation

Publication Date: 2021.03.30 ACCENTURE GLOBAL SOLUTIONS LTD

AI summary

A robotic process automation (RPA) system provides solutions to issues generated during processing of entities in an application stack including a plurality of applications. The data regarding an issue is analyzed to categorize the issue as a new issue or an existing issue. For existing issues, solutions to resolve the issues are identified from a knowledge base. Even if the issue is categorized as a new issue, an identification of a solution from the knowledge base is initially attempted. If no solution can be identified, then the new issue is presented for user input and the solutions based on the user input are implemented for the resolution.