Anticipatory Networking Reduces Latency via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current wireless communication technologies face significant challenges in reducing network latency, particularly due to round trip delays associated with dependent downstream operations, which hinder the performance of modern wirelessly-enabled devices.
Innovation Solution
The implementation of anticipatory networking techniques, which involve detecting user actions or device conditions, learning future operations needed, and proactively performing downstream operations before they are actually required, using machine learning to optimize network usage and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If downstream operations are performed sequentially after data requests are made, then network protocols and device operations are simple to implement, but network latency increases significantly
Solution Approach 1:
The patent applies preliminary action by performing downstream operations before the data is actually needed. The system proactively initiates operations such as obtaining location data, generating advertisements, and retrieving account information in advance, so that when the user interacts with the application, the data is already available and network latency is reduced.
Solution Approach 2:
The patent implements dynamics by dynamically adjusting the timing and execution of downstream operations based on predicted user behavior and application context. The system uses machine learning to determine which operations to perform proactively and when to execute them, optimizing the balance between reducing latency and managing device complexity adaptively.
2Duration of action of moving object
If multiple downstream operations are performed sequentially, then device power consumption is reduced, but application start-up time and refresh time increase
Solution Approach 1:
The system performs downstream operations in advance during periods when the device is idle or when power consumption is less critical. By pre-obtaining location data, generating advertisements, and retrieving account information before the application is launched, the system ensures fast start-up times without requiring all operations to run simultaneously, thus managing power consumption effectively.
Solution Approach 2:
The patent employs periodic action by scheduling downstream operations at specific intervals or triggers rather than continuously. The system uses machine learning to determine optimal times to perform operations, such as when the device is charging or during low-activity periods, thereby balancing application start-up performance with power consumption constraints.
3Productivity
If data requests are made only when needed, then device complexity is minimized, but network latency and user experience deteriorate
Solution Approach 1:
The system proactively performs downstream operations based on predicted user needs rather than waiting for explicit data requests. Machine learning algorithms analyze user behavior patterns and application context to determine which operations should be performed in advance, such as obtaining location data before a navigation app is opened or generating advertisements before a shopping app is launched, thereby improving productivity without significantly increasing complexity.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors user interactions and application performance to refine its predictions about which downstream operations to perform proactively. This feedback loop allows the system to optimize the balance between productivity improvement and complexity management by learning from actual user behavior and adjusting its anticipatory operations accordingly.
Data Source
AI summary
Embodiments for performing an anticipatory networking are provided. These embodiments include detecting an action taken by a user of a wirelessly-enabled device, an automated action of the wirelessly-enabled device, or a current condition of the device; learning what future operations the wirelessly-enabled device will likely need to perform in order to carry out the desired user action or device action; creating a user profile based on the learned information; and proactively performing, based on the user profile, certain downstream operations before the data corresponding to those operations is actually needed. In some embodiments, the anticipatory networking techniques disclosed herein essentially represent the confluence of networking concepts and machine learning concepts, and as such, enable wireless communications having reduced latency, while also improving network reliability and device performance.