Resource Bandwidth Monitoring with AI Prompt Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning or artificial intelligence-based models struggle to reliably and efficiently respond to resource availability queries due to frequent changes in constraints and regulations, leading to inaccurate and unreliable system responses and prolonged downtime from repeated retraining.
Innovation Solution
A model-based resource availability platform using generative AI models and threshold-based validation to process resource queries, dynamically constructing data structures and generating prompts to ensure accurate and reliable responses without frequent retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are retrained frequently to account for constraint changes, then response accuracy is improved, but system downtime and computational resource consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-defining multiple validation thresholds and constraint rules before they are needed for actual query processing. When constraints change, the system updates these pre-established validation parameters rather than retraining the entire model, thereby maintaining accuracy while avoiding prolonged downtime.
Solution Approach 2:
The patent introduces an intermediary validation layer between the machine learning model and the final response. This validation mechanism acts as a mediator that checks model outputs against updated constraints and regulations, ensuring accuracy without requiring model retraining. The validator serves as a buffer that absorbs the impact of constraint changes.
2Measurement precision
If machine learning models are retrained frequently to account for constraint changes, then response accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The patent extracts the validation function from the machine learning model itself, separating it as an independent threshold-based validation layer. This extraction allows the model to remain computationally efficient while the lightweight validation layer handles constraint verification, significantly reducing the computational resources needed compared to full model retraining.
Solution Approach 2:
The system uses simple, lightweight validation thresholds and constraint rules that can be quickly updated and discarded when changes occur, rather than investing in expensive, time-consuming model retraining. These validation parameters are inexpensive to compute and can be rapidly adjusted to accommodate new regulations or constraints.
3Reliability
If threshold-based validation is added to verify model outputs, then response reliability is improved, but system complexity increases
Solution Approach 1:
The patent segments the system into distinct functional components: the machine learning model for pattern recognition, the threshold validation layer for constraint verification, and the response generation module. This segmentation allows each component to be simple and specialized, with the validation layer acting as a straightforward checklist that does not significantly increase overall system complexity.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A status and monitoring platform for resource bandwidth is provided. A system can retrieve, responsive to a request for bandwidth of a resource, a data set that can include at least one constraint related to the resource and historic utilization of the resource. The system can construct, based on the data set, a data structure to replace the request. Based on the data structure, the system can generate a prompt indicating the constraint and the historic utilization. The system can identify, based on the prompt, a model trained with generative artificial intelligence to determine resource bandwidth. The system can input the prompt into the model to generate an output that indicates the bandwidth of the resource and validate the output based on a comparison with a threshold. The system can transmit for display, via an interface, responsive to the validation, an indication of resource bandwidth output by the model.