Latent Cause Disaggregation for Computer System Control
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Solution Overview
Problem
Conventional computer system control systems only process data values and fail to optimize system performance by considering latent causes, leading to suboptimal resource utilization and inefficiencies.
Innovation Solution
A computer system control system that processes both data values and descriptors of latent causes using a data disaggregation machine learning model and a control system machine learning model, generating commands to improve system performance by optimizing resource usage and reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional control systems process only data values, then the system complexity is lower, but the performance optimization and resource utilization are suboptimal
Solution Approach 1:
The patent segments the control system into two distinct models: a data disaggregation model that separates data values from latent cause descriptors, and a control system model that processes both separately. This segmentation allows the system to handle complex information (latent causes) without proportionally increasing overall complexity, as the disaggregation model pre-processes and structures the input data into manageable components.
Solution Approach 2:
The patent introduces a new dimension by extracting latent cause descriptors that represent underlying patterns beyond the raw data values. This additional dimensional representation allows the control system to capture hidden relationships and dependencies, improving performance optimization while the structured approach to this new dimension prevents uncontrolled complexity growth.
2Use of energy by moving object
If the control system processes latent cause descriptors, then energy efficiency and resource utilization improve, but the computational resources required increase
Solution Approach 1:
The data disaggregation model performs preliminary action by pre-processing the input data to extract latent cause descriptors before the control system model processes them. This pre-extraction of meaningful features reduces the computational burden during actual control operations, as the system works with already-structured descriptors rather than raw data, thereby improving energy efficiency without proportionally increasing total computational requirements.
Solution Approach 2:
The patent creates compressed representations (copies) of the underlying latent causes through descriptors that capture essential patterns without requiring full processing of original data. These descriptor copies allow the control system to work with simplified representations of complex phenomena, reducing real-time computational power requirements while maintaining the ability to optimize energy efficiency based on latent cause understanding.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for disaggregating latent causes for computer system optimization. In one aspect, a method includes accessing a data stream for data values resulting from operations performed by a computer system; providing the data values as input to a data disaggregation machine learning model that generates descriptors of latent causes of the data values; providing the data values and the descriptors of the latent causes of the data values as inputs to a control system model that generates embedded representations of commands to modify the operations performed by the computer system; determining commands to modify the operations performed by the computer system based on the embedded representations of commands to modify the operations performed by the computer system; and providing the commands to the computer system.