Autonomous System Behavior Modification via Domain-Performance Correlations
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Solution Overview
Problem
Existing methods for determining correlations between system performance and operational domain parameters are costly and time-consuming, particularly for complex systems operating in varied environments, limiting the ability to optimize system performance.
Innovation Solution
Utilizing systems, neural networks, and machine learning models to automatically determine correlations between operational domain parameters and performance indicators, enabling data preprocessing and alignment to identify correlations and modify system aspects accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data comparison methods are used to determine correlations between system performance and operational domain parameters, then measurement precision may be achieved, but productivity is significantly reduced due to the expensive and time-consuming nature of manual comparisons
Solution Approach 1:
The patent replaces manual mechanical comparison methods with automated computational systems including machine learning models and neural networks. These systems automatically ingest operational domain parameters and performance indicators, perform correlations through algorithmic processing, and generate insights without human intervention, thereby maintaining precision while dramatically increasing productivity
Solution Approach 2:
The patent introduces intermediate computational layers including data preprocessing modules, feature extraction systems, and machine learning models that act as mediators between raw data and correlation results. These intermediaries automate the complex comparison tasks that would otherwise require manual analysis, enabling high-throughput processing while preserving measurement accuracy through systematic computational approaches
2Measurement precision
If the number of performance indicators and operational design parameters increases to account for system complexity, then measurement precision improves, but the time required for manual comparisons increases exponentially
Solution Approach 1:
The patent segments the complex correlation analysis task into multiple independent processing stages: data ingestion, preprocessing, feature extraction, model inference, and result generation. Each stage handles specific subsets of parameters and indicators independently through parallel processing, allowing the system to manage large numbers of variables without exponential time increases
Solution Approach 2:
The patent transforms the correlation analysis problem by changing parameters from manual comparison metrics to computational efficiency metrics. Machine learning models process thousands of parameter combinations simultaneously using vectorized operations and optimized algorithms, converting what would be sequential manual comparisons into parallel computational tasks that scale linearly rather than exponentially with system complexity
3Productivity
If automated systems are implemented to determine correlations, then productivity increases, but device complexity increases due to the need for machine learning models and data processing infrastructure
Solution Approach 1:
The patent implements universal data processing components that handle multiple functions: the same machine learning infrastructure processes different types of operational parameters, the same neural network architecture analyzes various performance indicators, and the same computational pipeline performs both preprocessing and inference. This multi-functionality increases productivity while managing complexity through code reuse and standardized interfaces
Solution Approach 2:
The system incorporates self-service capabilities where the automated correlation system continuously ingests new data, retrains models, and updates correlations without external intervention. The system self-manages its complexity through automated model selection, hyperparameter tuning, and performance monitoring, reducing the operational burden despite increased architectural complexity
Data Source
AI summary
Embodiments of the present disclosure may relate to a method of modifying system behavior based on one or more determined correlations. In some embodiments, the method may include obtaining first data and second data where the first data may include one or more performance indicator values that may correspond to the performance of the system and where the second data may include one or more operational domain parameters that may correspond to the system. In some embodiments, the method may additionally include assembling a data structure based on the first data and the second data. In some embodiments, the method may additionally include determining one or more correlations between individual operational domain parameters and individual performance indicator values based on the assembled data structure. In some embodiments, the method may additionally include modifying one or more aspects of the system based on the determined correlations.