Personalized Content Workflow for Real-Time Interaction Prediction

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Solution Overview

Problem

Existing content creation systems lack the ability to efficiently and effectively design and create multimedia content customized to each end-user's preferences, including the manner in which the multimedia content is arranged when reproduced at the end-user devices, with current testing methodologies being lengthy and dependent on human involvement.

Innovation Solution

A machine learning-based system that generates bespoke content in real-time, tailored to each user's preferences, by monitoring interactions and predicting future interactions to create optimized multimedia content on-the-fly, using a bespoke content generator equipped with machine learning platforms and interfaces.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional content creation systems are used to create multimedia content, then content can be delivered to end-users, but the content cannot be customized to each individual end-user's preferences and the testing process is lengthy

Engineering Contradiction:

Improvecontent customization to individual user preferencesVSAvoidtesting time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system enables self-service by allowing the content generation system to automatically create and test customized content for each user without human intervention. The ML platform autonomously generates bespoke content tailored to individual user preferences, performs testing, and delivers the optimized content, eliminating the need for manual content creation and testing processes.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces traditional mechanical content creation processes with machine learning-based automated systems. Instead of manual content design and lengthy A/B testing, the system uses ML algorithms to generate and optimize content automatically, substituting human-driven mechanical processes with intelligent automated systems that operate at scale and speed.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If traditional content creation systems are used, then content can be delivered, but the systems lack the ability to create content on-the-fly tailored to each user

Engineering Contradiction:

Improvereal-time content generation speedVSAvoiduser-specific customizability

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system performs preliminary action by pre-training machine learning models on user preference data and interaction patterns before actual content delivery. The ML platform is prepared in advance with the capability to rapidly generate customized content for any user, enabling real-time personalization without sacrificing the quality and accuracy of user-specific adaptations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system utilizes parameter changes by dynamically adjusting content parameters based on individual user profiles and real-time interactions. The ML platform modifies content attributes such as formatting, media selection, and presentation style according to each user's demonstrated preferences, enabling rapid customization while maintaining high productivity through automated parameter optimization.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If manual testing methodologies are used for content optimization, then content can be validated, but the process is lengthy and dependent on human involvement

Engineering Contradiction:

Improvecontent optimization accuracyVSAvoidtesting duration

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces manual testing methodologies with automated machine learning-based validation systems. The ML platform automatically tests content variations, measures performance metrics, and determines optimal content configurations without human intervention, maintaining scientific rigor and reliability while reducing testing duration from days or weeks to minutes or seconds.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system implements continuous testing and optimization through automated feedback loops. Rather than discrete manual testing cycles, the ML platform continuously monitors content performance, learns from user interactions, and iteratively improves content optimization accuracy in real-time, ensuring reliable results are achieved rapidly through uninterrupted automated validation processes.

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS12547669B2Artificial intelligence-based personalized content creation workflow

Publication Date: 2026.02.10 NOSTRA INC

AI summary

A system and methodology for creating bespoke content tailored to each user in a user environment, including a bespoke content generator configured to autogenerate and test bespoke content in real-time and at least one machine learning platform. The at least one machine learning platform is configured to: autogenerate a landing webpage based on an interest level of all previously converted users from a same or similar followed generated multimedia content; monitor interaction with the landing webpage by a communicating device; and autogenerate on-the-fly and in real-time one or more subsequent webpages based on the interaction. The subsequent webpages are generated as the communicating device interacts with each webpage and progresses according to a predicted interaction trajectory.