Video Signature Analysis for Visual Content Recommendations

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Solution Overview

Problem

Existing content recommendation systems rely on manually labeled bibliographic data, which limits their ability to accurately reflect the diverse visual characteristics of content items, leading to suboptimal recommendations.

Innovation Solution

A deep recommendation system that analyzes video signatures using machine learning models, generating feature vectors based on texture, shape intensity, and temporal data from video frames to provide more accurate recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manually labeled bibliographic data is used for content recommendations, then the system is simple to operate and implement, but the recommendation accuracy is limited and cannot reflect diverse visual characteristics

Engineering Contradiction:

Improverecommendation accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces manual labeling mechanisms with automated machine learning-based video signature analysis. Instead of relying on human-annotated bibliographic data, the system automatically extracts visual features (texture, shape, color, motion) from video frames using deep learning models, thereby eliminating the bottleneck of manual labeling while maintaining high recommendation accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent transforms the representation of content items from discrete bibliographic parameters (genre, actor, director) to continuous visual feature vectors derived from video frames. By converting visual characteristics into quantitative parameters through image processing and machine learning, the system achieves more precise and nuanced recommendations that capture subtle visual patterns

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If video signature analysis using machine learning is implemented, then recommendation accuracy based on visual characteristics is improved, but computational resources and processing time increase

Engineering Contradiction:

Improvevisual characteristic analysis accuracyVSAvoidcomputational energy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent segments the video content into individual frames and analyzes each frame's visual characteristics independently using machine learning models. By processing videos frame-by-frame rather than analyzing entire videos at once, the system reduces computational complexity and energy consumption while maintaining comprehensive visual analysis capability

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies machine learning models selectively to extract only the most relevant visual features (texture, shape, color, motion) rather than processing every possible visual attribute. This partial analysis approach focuses computational resources on the most impactful features for recommendation accuracy, reducing overall energy consumption

Inventive Principle:

Principle #16Partial or excessive action

3Loss of information

If deep learning models analyze texture and shape intensity from video frames, then the ability to capture quantitative visual characteristics is enhanced, but the difficulty of detecting and measuring visual features increases

Engineering Contradiction:

Improvevisual characteristic information retentionVSAvoidvisual feature extraction difficulty

Core Design Contradiction:

Loss of informationVSDifficulty of detecting and measuring

Solution Approach 1:

The patent introduces machine learning models as intermediary systems that bridge the gap between raw video frames and extractable visual features. These models serve as mediators that automatically detect, measure, and represent complex visual characteristics (texture, shape, color, motion) in a standardized format, eliminating the need for complex manual measurement processes

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250294216A1Systems and methods for deep recommendations using signature analysis

Publication Date: 2025.09.18 ADEIA GUIDES INC

AI summary

Systems and methods are described herein for providing content item recommendations based on a video. Using feature vectors corresponding to at least one frame of a video (e.g., generated based on texture and shape intensity of a frame), a recommendation system improves content recommendation using analytic and quantitative characteristics derived from a frame of a content item rather than merely manually labeled bibliographic data (e.g., a genre or producer). The recommendation system may generate a feature vector based on a texture, a shape intensity (e.g., generated from a Generalized Hough Transform), and temporal data corresponding to at least one frame of a video. The feature vector is analyzed using a machine learning model (e.g., a neural network) to produce a machine learning model output. The recommendation system causes a recommended content item to be provided based on the machine learning model output.