Predictive Network Capacity Scaling for Customer Interest Spikes

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Solution Overview

Problem

Existing data networks struggle to accommodate sudden spikes in high bandwidth service usage without compromising customer experience, often resorting to degrading content quality through compression techniques.

Innovation Solution

Predict customer interest in future data content using search and consumption patterns, flag likely content, and dynamically scale network resources to meet anticipated demand without compressing content quality.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If network capacity is increased to accommodate traffic spikes, then customer experience is improved, but network infrastructure cost increases

Engineering Contradiction:

Improvecustomer experienceVSAvoidnetwork infrastructure

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system performs preliminary actions by predicting future customer interest in data content using search patterns and consumption history before the actual content consumption occurs. This allows the network to proactively allocate resources and scale capacity in advance of traffic spikes, ensuring customer experience is maintained without requiring permanent over-provisioning of network infrastructure.

Inventive Principle:

Principle #10Preliminary action

2Productivity

If content compression is applied to maintain network performance, then bandwidth usage is reduced, but content quality deteriorates

Engineering Contradiction:

Improvebandwidth usageVSAvoidcontent quality

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The system dynamically adjusts network resource allocation based on predicted customer interest and actual content consumption patterns. Rather than applying static compression to all content, the network scales capacity dynamically for high-interest content while maintaining original quality, and uses compression only when appropriate based on real-time conditions, thus avoiding universal quality degradation.

Inventive Principle:

Principle #15Dynamics

3Ease of operation

If network resources are allocated based on historical usage, then resource allocation is simplified, but responsiveness to sudden demand changes is reduced

Engineering Contradiction:

Improveresource allocationVSAvoidresponsiveness to demand

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The system implements feedback mechanisms by continuously monitoring customer search patterns, consumption history, and actual content usage. This feedback loop allows the network to adjust resource allocation in real-time based on predicted and actual demand, combining the simplicity of automated allocation with high responsiveness to sudden demand changes through data-driven adjustments.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12603850B2Predictive network capacity scaling based on customer interest

Publication Date: 2026.04.14 AT&T INTELLECTUAL PROPERTY I L P

AI summary

In one example, the present disclosure describes a device, computer-readable medium, and method for scaling network capacity predictively, based on customer interest. For instance, in one example, a method includes predicting an interest of a first customer in data content that will be available for consumption over a data network at a time in the future, wherein the predicting is based on customer data including at least a search pattern associated with the first customer, flagging the data content when the predicting indicates at least a threshold degree of likelihood that the first customer will be interested in the data content, and scaling an allocation of resources of the data network to the first customer, based on the flagging.