AI Bandwidth Metric System for Dynamic Event Modification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems fail to modify scheduled events based on bandwidth availability for a plurality of devices across a disparate computer network due to the lack of a mechanism to determine when user devices may lack bandwidth availability.
Innovation Solution
The system generates high-quality training data by collecting bandwidth metrics from historic network conditions and event modifications, then trains an artificial intelligence model to determine available bandwidth for user devices. This model is used to assess whether user devices are likely to be available for upcoming events based on device-specific network conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If artificial intelligence models are used to determine bandwidth availability for event modification, then the ability to process data and perform real-time determinations is improved, but the requirement for high-quality and abundant training data creates implementation complexity
Solution Approach 1:
The system collects and stores bandwidth metrics and network condition data in advance as training data before the AI model is deployed. Historical network conditions, device information, and event modification data are accumulated and prepared beforehand, allowing the AI model to be trained offline and then used for real-time automated decisions without requiring complex data collection during operation.
2Measurement precision
If specialized knowledge is required to design and integrate artificial intelligence-based solutions, then model accuracy can be improved, but the amount of people and resources available to create these solutions is reduced
Solution Approach 1:
The AI model automatically collects its own training data from the network environment it operates in. The system self-trains by processing historical network conditions, device bandwidth metrics, and event modification patterns that are naturally generated during normal operation, reducing the need for manual data collection and expert intervention in the training process.
3Productivity
If the process by which AI results are made is obscured, then real-time determination speed is improved, but the ability to identify errors and improve models is reduced
Solution Approach 1:
The system implements feedback loops where actual event modification outcomes are collected and used to retrain and improve the AI model. User decisions regarding event modifications based on AI recommendations are fed back into the training dataset, allowing the model to learn from real-world outcomes and improve accuracy over time while maintaining fast real-time performance.
Data Source
AI summary
Methods and systems are described herein for modifying scheduled events based on bandwidth availability. To modify the scheduled event, the system determines a plurality of user devices corresponding to an event. The system receives first network conditions for a first user device and generates a feature input based on the first network conditions. The system inputs the feature input into an artificial intelligence model to determine a first available bandwidth metric and aggregates the first available bandwidth metric with respective available bandwidth metrics for other user devices of the plurality of user devices to determine a composite available bandwidth metric. The system can compare the composite available bandwidth metric to a threshold available bandwidth metric and in response to determining that the composite available bandwidth metric equals or exceeds the threshold available bandwidth metric, the system generates for display, on a user device interface, a recommendation for modifying the event.