Hosting Network System for Automated Content Acquisition

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Solution Overview

Problem

Current web content publishing and hosting methods lack efficiency in gathering, organizing, and displaying digital content effectively, leading to reduced user engagement and revenue generation for content publishers.

Innovation Solution

A hosting network system that automatically identifies and hosts potentially valuable content by analyzing user engagement metrics, generates invitations to content publishers with estimated revenue potential, and personalizes content based on user interests to increase user interaction and advertising revenue.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If content publishers host their own content, then they have control over their content, but they generate less revenue and have reduced user engagement

Engineering Contradiction:

Improvecontent controlVSAvoidrevenue generation

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent introduces a hosting network as an intermediary between content publishers and users. The hosting network receives content from publishers, analyzes it using machine learning models, and distributes it to users based on predicted engagement. This intermediary structure allows publishers to maintain content control while the network optimizes distribution to maximize revenue and user engagement through data-driven decisions.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If manual content selection is used, then content can be carefully chosen, but the process is time-consuming and less scalable

Engineering Contradiction:

Improvecontent selection accuracyVSAvoidcontent acquisition time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual content selection processes with automated machine learning models. The system uses supervised learning algorithms that analyze historical data about user engagement, content performance, and advertising revenue to automatically predict which content will be most valuable. This mechanical substitution eliminates time-consuming manual analysis while maintaining or improving selection accuracy through data-driven insights.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The machine learning system performs self-service by automatically analyzing content, predicting performance metrics, and making acquisition decisions without human intervention. The system continuously learns from past performance data and autonomously optimizes content selection to maximize revenue and engagement, freeing publishers from time-consuming manual processes.

Inventive Principle:

Principle #25Self-service

3Productivity

If personalized content is provided, then user engagement increases, but the system complexity increases

Engineering Contradiction:

Improveuser engagementVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the content acquisition and delivery system into distinct functional modules: content ingestion, machine learning analysis, user profiling, content matching, and delivery. This segmentation allows each component to handle specific tasks independently, making the overall complex system more manageable and scalable. The machine learning models are divided into separate prediction modules that can be trained and deployed independently.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11132417B2Web content publishing acquisition

Publication Date: 2021.09.28 RPX CORP

AI summary

Briefly, embodiments disclosed herein relate to acquisition of web content publishing.