Cross-Platform Recommendation Engine for Group Media Discovery

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Solution Overview

Problem

Users face challenges in navigating the vast variety of content offerings from different providers and creators, with existing systems failing to integrate preference tracking across multiple platforms and lacking group-based content recommendations.

Innovation Solution

A recommendation integration system that collects preference information from users and groups, using gestures on touchscreens to make cross-platform content suggestions, and employs a cultivation engine to analyze user decisions and behaviors for personalized and group content recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If content recommendations are provided across multiple platforms, then user experience and satisfaction are enhanced, but system complexity increases due to integration requirements

Engineering Contradiction:

Improveuser experienceVSAvoidsystem complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The patent merges recommendation systems across multiple content platforms into a unified integration system. The recommendation engine aggregates user preference data from diverse sources (streaming services, social media, e-commerce) and generates coordinated recommendations that work across platforms, reducing the complexity of managing separate recommendation systems while enhancing user experience through consistent, multi-platform content suggestions.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system creates a universal recommendation engine that serves multiple functions across different platforms. The same core recommendation algorithm and user profile system operate in streaming services, social media, and e-commerce platforms, allowing one system architecture to handle diverse content types and delivery channels without requiring platform-specific recommendation implementations.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If group-based content recommendations are implemented, then collaboration and shared decision-making improve, but data processing requirements increase

Engineering Contradiction:

Improvegroup collaborationVSAvoiddata processing

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The system segments group recommendation processing into distinct operational modes: individual user preference tracking, group preference aggregation, and consensus-based recommendations. This segmentation allows the system to handle group data by processing it through standardized individual preference profiles and then aggregating results, reducing the computational burden of analyzing entire group behaviors as a single complex data structure.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces group preference profiles as intermediary structures that mediate between individual user data and group-level recommendations. These profiles aggregate and summarize preferences from multiple users, serving as intermediate data representations that reduce the complexity of processing raw group behavior data while enabling collaborative recommendation generation.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If user preference tracking is integrated across platforms, then recommendation accuracy improves, but implementation complexity increases

Engineering Contradiction:

Improverecommendation accuracyVSAvoidimplementation complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system standardizes preference tracking by transforming diverse platform-specific interaction data into unified parameter representations. User interactions across different platforms (streaming, social media, e-commerce) are converted into consistent preference parameters and profiles, enabling accurate cross-platform recommendation while simplifying implementation through standardized data formats and processing protocols.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250373872A1Streaming platforms and websites media discovery engine and recommendation integration system and method

Publication Date: 2025.12.04 SWIPEFINDER AB

AI summary

A method for integrating streaming platforms, website search engines, and social media is disclosed. The method may include a computer system sending a particular content recommendation to a user's device. The computer system may receive information corresponding to the user's decision regarding their interest in the particular content recommendation, and may determine a different content recommendation using a history of the user's decisions regarding other content recommendations. The computer system may then send the different content recommendation to the user's device.