Cloud Payload Management With ML-Triggered Utilization Forecasts
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Solution Overview
Problem
Existing payload allocation systems face challenges in accurately predicting utilization rates due to dynamic consumer behavior, inconsistent data on shelf conditions, and lack of comparative data from competitors, leading to issues like overstocking or stockouts, especially during short-term demand spikes from events.
Innovation Solution
A utilization prediction method using machine learning techniques that preprocesses temporal, geospatial, and demographic datasets, predicts payload utilization rates, schedules transmission, maintains buffer payloads, and validates outcomes against external telemetry to ensure accurate inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional heuristic methods are used for payload allocation, then the system is simple to operate, but the prediction accuracy of utilization rates deteriorates due to dynamic consumer behavior and sudden events
Solution Approach 1:
The patent replaces conventional heuristic methods with a machine learning-based prediction system that processes temporal, geospatial, demographic, and storage datasets to accurately forecast payload utilization rates, thereby improving prediction accuracy while maintaining operational simplicity through automated model-driven decisions
Solution Approach 2:
The patent introduces an intermediary machine learning engine that acts as a mediator between raw data sources and payload allocation decisions, processing and analyzing multiple data types to generate accurate utilization rate predictions that inform distribution strategies
2Measurement precision
If advanced machine learning techniques are implemented for payload prediction, then the prediction accuracy improves, but the device complexity increases due to multiple data processing steps and model components
Solution Approach 1:
The patent segments the complex prediction system into distinct functional modules: data preprocessing components that handle temporal, geospatial, demographic, and storage datasets separately; a machine learning engine for prediction; and a payload allocation system, making the overall complex system manageable and maintainable through clear separation of concerns
Solution Approach 2:
The patent creates a universal machine learning engine that handles multiple data types (temporal, geospatial, demographic, storage) and performs various functions including prediction, analysis, and decision support, reducing device complexity by consolidating multiple specialized components into a single multi-functional system
3Reliability
If buffer payload is maintained at nodes to offset prediction errors, then the reliability of inventory availability improves, but the loss of substance increases due to excess stock that ties up capital
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model continuously learns from actual payload utilization outcomes and prediction errors, adjusting future predictions to reduce the need for buffer stock while maintaining reliable inventory availability, thereby minimizing excess stock accumulation
Solution Approach 2:
The patent performs preliminary action by maintaining calculated buffer payloads at nodes based on predicted utilization rates and confidence intervals, positioning inventory in advance at optimal locations to meet demand while minimizing excess stock through data-driven buffer sizing rather than arbitrary overstocking
Data Source
AI summary
A utilization prediction method to manage payload allocation at a cloud network. The utilization prediction method involves preprocessing datasets, including temporal, geospatial, demographic, and storage-based datasets. The utilization prediction method further involves interpolating and modulating the datasets associated with an instance via a machine learning engine. The machine learning engine determines the magnitude of the instance, predicts a payload utilization rate, determines nodes at a map for payload allocation, and schedules payload transmission across the nodes within a time frame. The machine learning engine further triggers payload transmission based on a prediction outcome, maintains a buffer payload at nodes, monitors the payload utilization rate at a stage gate, and transforms the prediction outcome based on input from the stage gate and a feedback loop. Finally, the utilization prediction method includes validating the prediction outcome against external telemetry and displaying the prediction outcome, nodes, and alerts at a user interface.