Entertainment Platform Personalization via ML-Driven Ad Insertion

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Solution Overview

Problem

Current entertainment platforms fail to provide a highly personalized and minimally disruptive user experience due to disruptive advertisements and lack of customization in content delivery, leading to unsatisfied user expectations for content discovery.

Innovation Solution

A system and method that utilize user profiles, historical usage data, and machine learning engines to provide hyper-personalized recommendations and interactive experiences, allowing seamless integration of e-commerce and minimal advertising within the entertainment platform, enabling users to engage with curated products and services while streaming media content.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of energy

If traditional broadcast television networks transmit content with intermittent commercial breaks, then advertising revenue is generated, but user experience is disrupted and content continuity is broken

Engineering Contradiction:

Improveadvertising revenueVSAvoiduser experience

Core Design Contradiction:

Loss of energyVSEase of operation

Solution Approach 1:

The patent extracts advertising content from traditional interruptive commercial breaks and transforms it into seamless, personalized ad-insertion that occurs during natural pauses or transitions in content delivery, eliminating disruptive interruptions while maintaining revenue generation

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system dynamically adjusts advertising delivery based on real-time user preferences, viewing context, and content type, transforming static commercial breaks into adaptive, personalized ad experiences that align with user expectations and minimize disruption

Inventive Principle:

Principle #15Dynamics

2Loss of energy

If internet- and mobile-based entertainment platforms deliver content with intermittent advertisements, then advertising revenue is generated, but content flow is interrupted and user engagement is reduced

Engineering Contradiction:

Improveadvertising revenueVSAvoidcontent flow continuity

Core Design Contradiction:

Loss of energyVSDuration of action of moving object

Solution Approach 1:

The system performs preliminary analysis of user profiles, viewing history, and content metadata before delivering advertising content, pre-positioning relevant ads that align with user preferences and content context to ensure seamless integration without interrupting content flow

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intelligent intermediary system that acts as a mediator between content delivery and advertising insertion, using machine learning models to select and timing ad delivery during natural content transitions, thereby maintaining content flow continuity while generating revenue

Inventive Principle:

Principle #24Intermediary (Mediator)

3Device complexity

If entertainment platforms provide generic content recommendations, then system complexity is reduced, but user personalization and content discovery are insufficient

Engineering Contradiction:

Improvesystem complexityVSAvoiduser personalization

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The patent segments the recommendation system into modular components including user profile analysis, content metadata processing, machine learning model inference, and real-time recommendation generation, allowing complex personalization to be achieved through coordinated simple modules rather than a monolithic complex system

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements continuous feedback loops where user interactions with recommended content are tracked and fed back into the machine learning models, enabling the system to learn and adapt user preferences over time, thereby improving personalization without proportionally increasing system complexity

Inventive Principle:

Principle #23Feedback

4Adaptability or versatility

If entertainment platforms implement hyper-personalized recommendations with interactive e-commerce options, then user engagement and content discovery are improved, but system complexity and data processing requirements increase

Engineering Contradiction:

Improveuser personalizationVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent creates a universal platform architecture that handles multiple functions including content delivery, personalized recommendations, interactive e-commerce, and advertising insertion through a unified machine learning-based recommendation engine, reducing overall system complexity by consolidating diverse functions into a multi-functional core system

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20230377023A1Systems and methods for user personalization and recommendations

Publication Date: 2023.11.23 NBTV CHANNELS LLC

AI summary

Systems and methods for user personalization and recommendation schemes that are matched to a user profile and provide a highly personalized, interactive experience for the user on an entertainment platform are disclosed. In one aspect of the invention, the highly personalized and interactive experience is facilitated through information from the user profile comprised of user-inputted information, historical data, and outputs from machine learning engines. In another aspect of the invention, the system is capable of outputting the highly-personalized and interactive recommendations onto a viewing screen while media content is continuously streaming on the same viewing screen.