Content Capacity Policies for Feasible Real-Time Delivery

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current content distribution systems face inefficiencies and computational waste due to the generation of dynamic digital content that cannot be feasibly delivered to client devices, leading to underutilization of resources and inability to reallocate them to other processes.

Innovation Solution

A content capacity system that optimizes computational resource allocation by determining content-location pairs and implementing resource capacity policies to identify the most efficient combinations of content types and distribution locations, preventing the generation of suboptimal or unfeasible content items.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If content distribution systems generate diverse types and variations of dynamic digital content using machine learning models and computational pipelines, then the variety and quality of candidate content improve, but computational resource consumption increases significantly

Engineering Contradiction:

Improvecontent varietyVSAvoidcomputational resource consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system pre-generates content-location pairs and pre-determines feasibility of delivering content from specific locations before actual content delivery requests arrive. This preliminary assessment prevents wasteful generation of content that cannot be delivered, reducing computational resource consumption while maintaining content variety options.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system extracts and removes infeasible content-location pairs from the candidate content pool before delivery. By identifying and eliminating content that cannot be delivered from specific locations, the system reduces computational waste on processing and attempting to deliver impossible content while preserving feasible content variations.

Inventive Principle:

Principle #2Taking out (Extraction)

2Productivity

If content distribution systems generate extensive candidate content using computational pipelines, then content quality and options improve, but resource allocation efficiency deteriorates

Engineering Contradiction:

Improvecontent generation outputVSAvoidresource allocation efficiency

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system performs preliminary feasibility determination for content-location pairs before full content generation. By assessing deliverability constraints in advance, the system avoids allocating computational resources to generate content that cannot be delivered, improving resource allocation efficiency while maintaining productive content generation for feasible options.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses feedback from feasibility assessments and delivery constraints to dynamically adjust content generation priorities. Content-location pairs that are feasible receive computational resources for generation, while infeasible pairs are excluded, creating a feedback loop that optimizes resource allocation efficiency based on actual deliverability outcomes.

Inventive Principle:

Principle #23Feedback

3Measurement precision

If content distribution systems allocate computational resources to generate all possible content variations, then content selection quality improves, but system flexibility to reallocate resources to other processes deteriorates

Engineering Contradiction:

Improvecontent selection qualityVSAvoidresource reallocation flexibility

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The system extracts and removes infeasible content-location pairs from the candidate pool, reducing the total computational burden. This extraction maintains content selection quality for feasible options while freeing up computational resources that can be reallocated to other processes, improving resource reallocation flexibility.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system generates content selectively for feasible location pairs rather than attempting to generate all possible content variations. This partial action approach maintains sufficient content selection quality for deliverable content while avoiding excessive resource consumption on infeasible options, enabling better resource reallocation flexibility.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20260056786A1Content type delivery management for computational capacity-constrained resource systems

Publication Date: 2026.02.26 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

This disclosure describes a content capacity system that provides a framework for solving computational capacity management problems for content providers that provide real-time content distribution. For example, the content capacity system generates and implements resource capacity policies that enable content providers to identify a target distribution location with available resources to provide optimal content types when applied to incoming content requests. Additionally, the content capacity system ensures that resources are efficiently used for the content type with the highest utility and avoids processing infeasible locations and content types, as well as exceeding computational capacities.