Media Content Descriptor Mapping for Target Group Identification

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current methods lack an efficient way to determine media content descriptors that match target profiles for personalized media recommendations and user targeting across various applications, such as media engines and advertising, due to limitations in semantic and emotional analysis.

Innovation Solution

A method that maps target profiles to media content descriptors using semantic and emotional descriptors, employing artificial intelligence models and machine learning techniques to analyze audio and video content, and selects the best matching media items based on similarity searches and aggregation of content features.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional media analysis methods are used, then the system is simple to implement, but the accuracy of matching media items to target profiles is insufficient

Engineering Contradiction:

Improvematching accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces AI models and semantic descriptors as intermediaries between media content and target profiles. These intermediaries transform raw media features into meaningful semantic representations that can be accurately matched with personality and emotional profiles, thereby improving matching accuracy while managing system complexity through modular architecture

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces traditional rule-based or manual media analysis methods with AI-driven semantic analysis systems. This substitution enables more accurate extraction of emotional and personality-related features from media content, significantly improving matching precision despite increased computational requirements

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If comprehensive semantic and emotional analysis is performed, then the quality of media profiles improves, but the processing time increases

Engineering Contradiction:

Improveprofile qualityVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent performs preliminary semantic analysis and generates media profiles in advance before they are needed for matching. By pre-processing media content into standardized semantic descriptors and emotional profiles, the system reduces processing time during actual recommendation or targeting operations while maintaining high profile quality

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent divides the comprehensive media analysis into separate modular components: semantic analysis, emotional analysis, and profile generation. This segmentation allows each component to be optimized independently and enables parallel processing, reducing overall processing time while maintaining comprehensive analysis quality

Inventive Principle:

Principle #1Segmentation

3Adaptability or versatility

If AI models and machine learning techniques are employed, then the semantic and emotional analysis capability improves, but the computational resources required increase

Engineering Contradiction:

Improveanalysis capabilityVSAvoidcomputational resources

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The patent dynamically adjusts analysis parameters such as model complexity, resolution of semantic extraction, and depth of emotional analysis based on requirements. This allows the system to use more sophisticated AI models when high adaptability is needed while switching to lighter models for routine tasks, optimizing computational resource utilization

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent applies comprehensive AI-based semantic and emotional analysis selectively only to media items that require detailed profiling, while using simpler methods for routine categorization. This partial application of resource-intensive analysis reduces overall computational burden while maintaining high adaptability where it matters most

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20230409633A1Identification of media items for target groups

Publication Date: 2023.12.21 TETRA HOLDINGS AUSTRALIA PTY LTD

AI summary

The disclosure relates to a method for determining a best matching media content descriptor set. The method comprises obtaining a target profile having a plurality of profile scores; mapping the target profile to a set of target content descriptors having a plurality of features, the mapping by applying at least one mapping rule that defines how a feature of a target content descriptor set is computed from profile scores; obtaining a plurality of media content descriptor sets, each media content descriptor set associated with a media item or a group of media items and having features comprising semantic descriptors for the respective media item or group of media items, the semantic descriptors comprising at least one emotional descriptor for the media item or group of media items; and searching for at least one media content descriptor set having the best matching content descriptor set with respect to the target content descriptor set.