Automation Framework Efficiency Score Calculation
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Solution Overview
Problem
Current automation platforms lack metrics for measuring the quality, robustness, and effectiveness of automated workflows, and fail to identify gaps such as network connectivity or training issues, making it difficult for SOC teams to improve automation health and handle different datasets.
Innovation Solution
A framework is developed to calculate an efficiency score for automation platforms by assigning weights to playbooks and error types, providing insights into failure reasons and predicting execution likelihood on different datasets, with a health dashboard displaying the automation framework's health based on these scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automation platforms execute multiple playbooks without metrics, then automation coverage is increased, but quality and robustness cannot be measured
Solution Approach 1:
The patent implements a feedback mechanism by calculating efficiency scores based on playbook execution metrics and error data. The system collects information about playbook successes, failures, and error types, then feeds this back into efficiency score calculations that provide measurable quality indicators for automation performance.
Solution Approach 2:
The patent replaces subjective quality assessment with an automated computational system that calculates efficiency scores using weighted metrics. Instead of manual evaluation of automation quality, the system uses algorithmic processing of execution data and error patterns to objectively measure automation robustness and effectiveness.
2Device complexity
If automation platforms lack error tracking, then system simplicity is maintained, but failure reasons cannot be identified
Solution Approach 1:
The patent segments error tracking by categorizing errors into distinct types (network connectivity errors, training errors, data errors, etc.). Each error type is separately tracked and weighted, allowing the system to maintain organized, manageable data structures while comprehensively capturing failure information across multiple dimensions.
Solution Approach 2:
The patent introduces an intermediary efficiency score calculation layer that processes raw error data and transforms it into meaningful insights. This intermediary system aggregates error information, applies weighting factors, and produces synthesized metrics that reveal failure patterns without requiring direct complex error analysis.
3Measurement precision
If weights are assigned to all playbooks and error types, then measurement precision is improved, but system complexity increases
Solution Approach 1:
The patent uses parameter changes by adjusting weight values assigned to different error types and playbooks based on their importance and impact. The system dynamically modifies these parameters to reflect changing priorities, allowing precise measurement of efficiency while managing complexity through configurable weight parameters rather than fixed complex structures.
4Device complexity
If automation platforms do not predict execution likelihood, then system simplicity is maintained, but adaptability to different datasets is reduced
Solution Approach 1:
The patent applies preliminary action by calculating efficiency scores and analyzing error patterns before automation executes on new datasets. The system uses historical performance data and weighted error tracking to predict potential execution outcomes, allowing proactive identification of issues before they affect new data processing tasks.
Data Source
AI summary
Systems and methods for determining an efficiency score for an automation platform are provided. According to one embodiment, a first weight for each playbook of multiple playbooks of an automation framework and a second weight for each type of error of multiple types of errors that may cause execution of one of the multiple playbooks to fail are maintained. The first weight represents a relative importance of the playbook and the second weight represents an effort required to address the error. An efficiency score is calculated for execution of one or more playbooks of the multiple playbooks during a particular time period based on the first weight for each of the one or more playbooks and the second weight for each type of error observed during the particular time period. An indication of a health of the automation framework is then displayed based on the efficiency score.