Media Subscription Package Recommendations Across Viewing Platforms

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing systems fail to provide personalized content recommendations based on user preferences across multiple platforms, leading to limited user engagement and missed opportunities for up-selling and cross-selling of channels and programs, due to inadequate data on individual viewing habits and preferences.

Innovation Solution

A system and method that captures user activities and preferences across various platforms, assigns weightages to these activities, and generates personalized recommendations for channels, packs, and VOD content, integrating with subscriber management systems to enhance sales and revenue through targeted marketing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional broadcast television systems are used, then infrastructure complexity is low and coverage is wide, but user engagement is limited and personalized recommendations cannot be provided

Engineering Contradiction:

Improveuser engagementVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

A recommendation engine is introduced as an intermediary component between the broadcast television system and the user. This engine processes viewing history data, applies algorithms to analyze patterns, and generates personalized recommendations. The intermediary layer enables personalized engagement without requiring fundamental changes to the core broadcast infrastructure, thus resolving the contradiction between adaptability and complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system segments user engagement into distinct functional modules: data collection (viewing history tracking), data processing (pattern analysis), recommendation generation (personalized content selection), and delivery (displaying recommendations). This segmentation allows each component to be optimized independently and facilitates scalable implementation, improving user engagement while managing system complexity through modular architecture.

Inventive Principle:

Principle #1Segmentation

2Productivity

If viewing history data is collected and processed to generate personalized recommendations, then user engagement and revenue increase, but data processing complexity and computational resources required increase

Engineering Contradiction:

Improverevenue generationVSAvoiddata processing complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system applies partial action by focusing data processing on the most relevant viewing history patterns rather than analyzing all possible viewing behaviors. The recommendation engine identifies and processes only the critical patterns (e.g., genre preferences, time-based viewing habits) needed for effective recommendations, reducing computational complexity while still achieving high revenue generation through personalized content delivery.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system changes processing parameters dynamically based on data volume and complexity. For new users or changing preferences, the system adjusts processing depth, selection thresholds, and algorithmic approaches. This adaptive parameter adjustment allows the system to maintain high productivity in revenue generation while managing data processing complexity through intelligent parameter optimization rather than linear scaling of computational resources.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If existing subscriber management systems are integrated with recommendation engines, then personalized marketing capability improves, but system integration complexity and implementation difficulty increase

Engineering Contradiction:

Improvepersonalized marketing capabilityVSAvoidsystem integration ease

Core Design Contradiction:

Adaptability or versatilityVSEase of manufacture

Solution Approach 1:

The recommendation engine is designed with universal interfaces that can integrate with multiple types of subscriber management systems (CRM, billing systems, content delivery systems). It provides standardized data input and output protocols, allowing the same core engine to serve different integration scenarios. This universality enables personalized marketing capability to be achieved through a single multi-functional component rather than requiring custom integration solutions for each system, thereby reducing overall integration complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The recommendation engine acts as an intermediary layer between existing subscriber management systems and the personalized marketing functionality. It receives standardized data from various subscriber management systems, processes it through unified algorithms, and delivers personalized recommendations through standardized output interfaces. This intermediary approach abstracts the complexity of system integration from the end application, enabling personalized marketing capability while maintaining ease of manufacture through standardized integration points.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250265632A1Method, computer-readable medium, and system for media subscription package management

Publication Date: 2025.08.21 WHAT S ON INDIA MEDIA PRIVATE LIMITED

AI summary

A method and system for sale management is disclosed. The method and system enhance a television service provider's ability to sell channels and packs to users. The system and method disclosed herein enables a user to receive recommendations for one or more television channels and/or channel packages based on one or more television viewing activities of the user.