AI Object Pixel Confidence for Dynamic Media Content Placement

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Solution Overview

Problem

Existing content pairing mechanisms, such as pre-roll, mid-roll, and sponsored posts, require prior allocation and cannot dynamically place content in real or near real-time within pre-existing content without prior knowledge of the media asset.

Innovation Solution

A system using artificial intelligence models to determine pixel count confidence of objects in media assets, allowing seamless insertion of content by identifying suitable objects for placement based on pixel count requirements, accounting for size, duration, and positional changes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If content is placed in pre-existing content using traditional mechanisms (pre-roll, mid-roll, sponsored posts), then content pairing is achieved, but content placement cannot be dynamic and must be selected and integrated prior to content creation

Engineering Contradiction:

Improvedynamic content placement capabilityVSAvoidtime for content selection and integration

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by analyzing media assets and identifying suitable objects for content placement before the actual content insertion. The AI model pre-evaluates pixel count confidences, object sizes, and positional characteristics to prepare placement candidates in advance, enabling dynamic decision-making when content needs to be inserted.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system transitions from static pre-planned content placement to dynamic real-time placement. The AI model continuously monitors media assets as they are displayed, tracking object positions, sizes, and pixel counts in real-time, allowing the system to adaptively select and place content based on current visual conditions rather than predetermined slots.

Inventive Principle:

Principle #15Dynamics

2Productivity

If the system identifies objects in real-time media assets, then dynamic content placement is enabled, but the system cannot have prior knowledge of objects, their suitability, or changes occurring to them

Engineering Contradiction:

Improvereal-time content placement efficiencyVSAvoidprior knowledge of objects and their characteristics

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The system implements feedback mechanisms by continuously monitoring media assets as they are displayed and using this real-time information to adjust content placement decisions. The AI model receives feedback about object positions, sizes, and pixel counts during playback, allowing iterative refinement of placement choices based on actual visual conditions rather than static pre-analysis.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system replaces traditional mechanical content placement mechanisms (fixed slots, predetermined positions) with an AI-based computational approach. The AI model processes visual data from media assets, calculates pixel count confidences, and makes intelligent placement decisions based on object characteristics and real-time conditions, substituting rigid mechanical systems with adaptive computational intelligence.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If the system uses AI models to determine pixel count confidence, then suitability of objects for content placement is accurately assessed, but the complexity of the system increases

Engineering Contradiction:

Improveaccuracy of object suitability assessmentVSAvoidsystem complexity for real-time analysis

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system focuses on changing key parameters such as pixel count, object size, and positional characteristics to assess suitability for content placement. By transforming complex visual data into measurable parameters like pixel count confidence scores, the AI model can systematically evaluate object suitability while maintaining computational efficiency through parameter-based decision-making rather than complex analysis.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12536723B2Systems and methods for dynamic content placement based on pixel count confidences in pre-existing content

Publication Date: 2026.01.27 REINBOW LLC

AI summary

Systems and methods for dynamic content placement based on pixel count confidences in pre-existing content. For example, the system may receive a first media asset. The system may determine a first pixel count requirement for the first media asset, wherein the first pixel count requirement indicates a required number of pixels appearing in objects for incorporating the first media asset. The system may identify a first object in a second media asset. The system may determine, using an artificial intelligence model, a first pixel count confidence that the first object meets the first pixel count requirement. The system may, in response to determining that the first pixel count confidence corresponds to the threshold pixel count confidence, generate for display, in a user interface, the first media asset at the first object while the second media asset is presented.