Media Trend Discovery System Using Modular Pattern Aggregation

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Solution Overview

Problem

Current systems fail to effectively discover and recommend trending online content to users based on their consumption patterns and contextual data, limiting personalized content delivery and engagement.

Innovation Solution

A processing system that obtains metadata from online media content, determines consumer context, and aggregates consumption patterns to identify trends, generating real-time recommendations for users by correlating trends with user profiles and activities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If current systems are used for content recommendation, then system simplicity is maintained, but personalized content delivery and user engagement are limited

Engineering Contradiction:

Improvepersonalized content deliveryVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the recommendation process into distinct modules: metadata extraction from content, consumer context determination from user data, consumption pattern aggregation across users, trend identification from aggregated patterns, and recommendation generation by correlating trends with individual profiles. This segmentation enables personalized content delivery while managing system complexity through modular design.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary actions by pre-aggregating consumption patterns from multiple users and pre-identifying trends before specific recommendation requests. User profiles are continuously updated with consumption patterns, and trends are detected in advance, enabling rapid personalized recommendation generation without complex real-time processing for each user query.

Inventive Principle:

Principle #10Preliminary action

2Loss of time

If real-time trend analysis is implemented, then content recommendation timeliness is improved, but data processing time and computational resources increase

Engineering Contradiction:

Improverecommendation response timeVSAvoidcomputational resources

Core Design Contradiction:

Loss of timeVSPower

Solution Approach 1:

The system performs preliminary aggregation of consumption patterns and preliminary identification of trends from aggregated data before specific recommendation requests arrive. By pre-processing and storing aggregated consumption patterns and identified trends, the system minimizes real-time computational requirements when generating individual recommendations, thus reducing response time while managing computational resource usage.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system processes data at appropriate levels of detail - aggregating consumption patterns across users to identify general trends, then correlating only relevant trends with individual user profiles for recommendation generation. This partial processing approach avoids unnecessary computational overhead while maintaining real-time recommendation capability.

Inventive Principle:

Principle #16Partial or excessive action

3Measurement precision

If comprehensive metadata and context data are collected, then recommendation accuracy is improved, but data privacy concerns and storage requirements increase

Engineering Contradiction:

Improverecommendation accuracyVSAvoiddata storage requirements

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The system extracts only the essential metadata from media content (such as genre, topic, and descriptive keywords) and essential consumer context information (such as consumption patterns and preferences) needed for accurate recommendations. By extracting and storing only these critical data elements rather than complete raw data, the system improves recommendation accuracy while minimizing data storage requirements and privacy concerns.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

Instead of collecting all possible user data and then filtering for relevance, the system inverts the approach by first identifying the essential metadata and context elements needed for accurate recommendations, then collecting only those specific data points. This inversion reduces data storage requirements while maintaining recommendation accuracy.

Inventive Principle:

Principle #13The other way round (Inversion)

Data Source

PatentUS11336743B2System for trend discovery and curation from content metadata and context

Publication Date: 2022.05.17 AT&T INTELLECTUAL PROPERTY I L P

AI summary

Aspects of the subject disclosure may include, for example, a method that includes obtaining metadata from media content and consumed by network subscribers; determining for each network subscriber a consumer context associated with the media content; and determining a media consumption pattern for each network subscriber based on the metadata and the consumer context, thereby generating a plurality of media consumption patterns. The method further includes aggregating the media consumption patterns; determining, based on the aggregated media consumption patterns, a media consumption trend for the network subscribers; and correlating the media consumption trend with a profile including a current activity for a network subscriber of the plurality of network subscribers, thereby generating a recommendation for the network subscriber regarding new media content not previously consumed by the network subscriber. The method also includes communicating the recommendation to the network subscriber. Other embodiments are disclosed.