Media Guidance Application Dynamic Listing Prioritization

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Solution Overview

Problem

Conventional media systems fail to accurately recommend media content listings based on real-time user interest, as their recommendations do not account for changes in user interest during content progression, leading to suboptimal user engagement.

Innovation Solution

A media guidance application that prioritizes media content listings by performing real-time statistical analysis of data and metadata, determining user interest through historical data comparisons, and adjusting the display of listings based on the likelihood of user engagement, promoting more engaging content and demoting less engaging ones.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional media systems use user interests and content metadata to generate recommendations, then users receive personalized listing suggestions, but the recommendations do not reflect real-time changes in user interest during content progression

Engineering Contradiction:

Improveaccuracy of user interest measurementVSAvoidtime delay in detecting user interest changes

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent implements dynamic prioritization of media content listings by continuously updating display prominence based on real-time user engagement metrics. The system transitions from static recommendations to dynamic prioritization where listing prominence adjusts automatically as user interest changes during content progression, resolving the contradiction between measurement accuracy and time delay

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system employs feedback loops that monitor user engagement metrics (viewing duration, interaction frequency, content progression rate) in real-time and use this feedback to adjust listing prioritization. This continuous feedback mechanism enables the system to detect and respond to user interest changes immediately, eliminating the time delay inherent in conventional batch-processing recommendation systems

Inventive Principle:

Principle #23Feedback

2Loss of information

If media systems display all media content listings equally, then users have access to complete content information, but users experience difficulty reviewing and selecting listings due to information overload

Engineering Contradiction:

Improvecompleteness of content informationVSAvoidease of listing selection

Core Design Contradiction:

Loss of informationVSEase of operation

Solution Approach 1:

The patent applies local quality by differentiating the display prominence of individual listings based on their real-time prioritization scores. Instead of uniform display, the system enhances the visual prominence (size, position, highlighting) of high-priority listings while reducing the prominence of low-priority ones, allowing users to quickly identify relevant content without being overwhelmed by equal-weighted information

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system segments the complete set of media content listings into priority-based groups (high, medium, low priority) and displays them with different levels of prominence. This segmentation organizes the information hierarchy, making it easier for users to focus on the most relevant listings first while still maintaining access to the complete content catalog

Inventive Principle:

Principle #1Segmentation

3Reliability

If media systems provide recommendations based on historical user preferences, then recommendations align with user interests, but the recommendations become inaccurate as user interest changes during content progression

Engineering Contradiction:

Improvereliability of recommendationsVSAvoidadaptability to real-time interest changes

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent transforms static recommendation systems into dynamic prioritization systems that adapt in real-time. The system continuously recalculates listing priority based on current user engagement metrics, allowing recommendations to evolve as user interest changes during content progression. This dynamic adaptation maintains reliability by grounding recommendations in actual observed behavior rather than static historical preferences

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs preliminary prioritization of listings before user selection based on predicted interest levels derived from historical data and current context. This preliminary ranking provides a head start on recommendation accuracy, which is then continuously refined in real-time as actual user engagement data becomes available, combining the benefits of both historical prediction and real-time adaptation

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS9094708B2Methods and systems for prioritizing listings based on real-time data

Publication Date: 2015.07.28 ROVI PRODUCT CORP

AI summary

Methods and systems for a media guidance application that can prioritize media content listings based on the real-time progression of the content associated with the listing. The media guidance application may interpret metadata concerning the progress of the content associated with the listing to determine whether or not a particular user will have, or continue to have, interest in the listing based on real-time statistical analysis.