Responsive Media Generation With Automated Visual-Object Arrangement

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Solution Overview

Problem

Existing methods for generating media are inefficient in creating responsive media that effectively engage users and adapt to various input modes, lacking a systematic approach to combine static visual objects into interactive formats.

Innovation Solution

A method involving a multi-dimensional feature space is defined to arrange static visual objects within media formats, with engagement metrics and operator feedback used to refine the media generation, allowing for automatic adaptation to user interactions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If static visual objects are manually assembled into media formats, then media generation precision is improved, but productivity deteriorates due to time-consuming manual processes

Engineering Contradiction:

Improvemedia generation precisionVSAvoidmedia generation productivity

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The system enables automatic self-assembly of media formats by using machine learning models to autonomously select and arrange static visual objects based on engagement metrics and style rules, eliminating manual assembly operations while maintaining high precision through automated optimization

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system transforms manual assembly parameters into automated decision-making parameters by using machine learning models that process engagement metrics, style rules, and visual object characteristics to automatically determine optimal media format configurations, thereby improving both precision and productivity

Inventive Principle:

Principle #35Parameter changes

2Device complexity

If media formats are fixed and static, then device complexity is reduced, but adaptability deteriorates in responding to user inputs and interactions

Engineering Contradiction:

Improvemedia format complexityVSAvoidmedia adaptability to user inputs

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The system introduces dynamic adaptability by enabling media formats to automatically adjust and respond to user inputs through machine learning models that process engagement metrics and style rules, allowing the media to evolve from static to dynamic while managing complexity through automated decision-making

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system achieves multi-functionality by designing a universal media generation system that can handle multiple user input types and interaction modes through a single automated framework, allowing one system to serve multiple adaptability requirements without proportionally increasing complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Productivity

If automated media generation is implemented, then productivity is improved, but manufacturing precision deteriorates due to lack of control over media arrangement

Engineering Contradiction:

Improvemedia generation productivityVSAvoidmedia arrangement precision

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The system implements feedback mechanisms by using engagement metrics to continuously monitor and evaluate media performance, then feeding this information back into the machine learning model to refine future media arrangements, thereby maintaining precision through iterative optimization while preserving high productivity

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system replaces manual mechanical control with automated machine learning-based control, using algorithms that process style rules and engagement metrics to automatically determine optimal media arrangements, thereby maintaining precision through intelligent decision-making rather than manual intervention

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250328726A1Method for automatically generating responsive media

Publication Date: 2025.10.23 YIELDMO

AI summary

A method includes: accessing a static visual objects, and media formats; defining a multi-dimensional feature space representing possible arrangements of combinations of the set of static visual objects within the set of media formats; generating a primary feature container distributed within the multi-dimensional feature space; generating a primary responsive media, by inserting the primary subset of static visual objects into the primary media format according to a primary arrangement of the primary subset of static visual objects represented in the feature container; presenting the primary responsive media to an operator; in response to receiving a selection of the primary responsive media generating a secondary feature container distributed within the multi-dimensional feature space proximal the primary feature container; generating a secondary responsive media, and serving the secondary responsive media to a first device for playback to a first user responsive to inputs by the first user at the first device.