RPA Execution Streaming for Real-Time Bot Visibility
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Solution Overview
Problem
The lack of visibility into robotic process automation (RPA) execution limits the ability to promptly detect, diagnose, and resolve issues, leading to increased operational costs and reduced efficiency due to wastage of computing resources and lengthy debugging processes.
Innovation Solution
A streaming solution is introduced to capture, buffer, and stream RPA execution, providing real-time or near-real-time insights into RPA workflows, allowing for rapid detection and resolution of issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If RPA executes without intervention, then automation efficiency is improved, but visibility into execution is lost
Solution Approach 1:
The patent introduces a streaming service as an intermediary between the RPA execution environment and users. This mediator captures execution details (screen recordings, logs, metrics) and transmits them to clients, enabling users to monitor automated processes without interfering with their execution. The streaming service acts as the intermediary that bridges the gap between automation and observability.
Solution Approach 2:
The patent creates visual copies of the RPA execution process through screen recordings and graphical representations. Instead of requiring users to directly observe the automated process, the system generates visual copies (video streams, graphical representations) that replicate the execution state, allowing users to monitor what the bot is doing without the bot needing to be manually guided.
2Loss of energy
If RPA continues executing erroneously, then computing resources are wasted, but debugging time increases
Solution Approach 1:
The patent implements real-time feedback mechanisms where execution details (including errors, warnings, and progress) are streamed back to users during RPA execution. This feedback loop enables users to monitor execution health and immediately identify failures, allowing them to terminate erroneous executions before they waste additional computing resources. The feedback mechanism directly addresses both resource wastage and debugging time by providing immediate visibility into execution status.
Solution Approach 2:
The patent enables preliminary detection of execution failures through real-time streaming of execution details. Users can identify issues during execution rather than after completion, allowing them to take corrective action (terminating faulty executions) before significant computing resources are consumed. This preliminary detection action prevents resource wastage and reduces debugging time by catching errors early in the execution lifecycle.
3Loss of information
If logs are generated for RPA execution, then execution tracking is improved, but parsing difficulty increases
Solution Approach 1:
The patent creates visual copies of execution data through screen recordings and graphical representations. Instead of requiring users to parse text logs, the system generates visual representations (video streams showing screen captures, graphical representations of execution flow) that make execution tracking intuitive and easy to understand. This visual copying approach eliminates the parsing difficulty while maintaining comprehensive execution tracking.
Solution Approach 2:
The patent employs visual indicators and graphical representations that use color coding and visual differentiation to represent various execution states, errors, and warnings. This visual encoding makes it easier for users to quickly identify execution issues without parsing text logs, as color changes and visual cues provide immediate, intuitive information about execution health and status.
Data Source
AI summary
Example implementations may include capturing virtual bot execution, for example by taking a screen recording of a desktop screen of a virtual machine where the execution is taking place. The contents, such as video such as the recording and/or associated data, may be processed, buffered, stored, and/or transmitted before being streamed (e.g., to a user). Thus, these implementations may involve initiating a virtual bot to execute an operation; generating a graphical representation of the virtual bot executing the operation; and transmitting, to a client device, the graphical representation of the operation for display at the client device.