Trip Content Preloading Based on Viewing Progress and Geo Access
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Solution Overview
Problem
Travelers face challenges in accessing desired content during trips due to geographical limitations and inefficient manual content downloading, which often results in insufficient episodes or shows to watch, and existing systems lack dynamic optimization based on consumption patterns and geographic restrictions.
Innovation Solution
A system that dynamically optimizes content for a scheduled trip by analyzing user consumption patterns, geographic content availability, and travel itineraries to automatically download and adjust media assets on a consumption device, ensuring adequate content is available throughout the trip.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the user manually downloads all episodes of a series before departing for the trip, then the user has sufficient content to watch during the trip, but the consumer device storage is filled up and the user cannot access content during the trip
Solution Approach 1:
The system automatically identifies content that should be downloaded before the trip based on the user's consumption pattern and the scheduled trip information, rather than requiring manual user action. This preliminary automated action ensures content is prepared in advance without filling the device unnecessarily.
Solution Approach 2:
The system monitors the user's consumption pattern and automatically adjusts content downloads without requiring manual intervention. The system serves itself by analyzing progress and determining optimal download timing, eliminating the need for user involvement in the content management process.
2Adaptability or versatility
If the user watches some episodes before departing and then manually downloads additional episodes, then the user has more content options, but the system becomes labor-intensive and complex
Solution Approach 1:
The system continuously monitors the user's consumption pattern and uses this feedback to dynamically adjust content downloads. By analyzing real-time progress data, the system automatically determines what content should be downloaded and when, creating a closed-loop system that adapts to user behavior without adding complexity.
Solution Approach 2:
The system dynamically adjusts content management strategies based on changing conditions such as consumption patterns and trip schedules. Rather than using static manual processes, the system adapts its behavior in real-time to optimize content availability while minimizing user effort.
3Productivity
If the system downloads content based on predicted consumption patterns, then the content is optimized for the trip, but the system must accurately predict consumption to avoid downloading insufficient content
Solution Approach 1:
The system uses actual consumption data as feedback to refine and improve prediction accuracy. By continuously monitoring what the user actually watches and comparing it to predictions, the system learns and adjusts its modeling to improve future predictions, making the system more accurate over time.
Solution Approach 2:
The system performs preliminary analysis of consumption patterns and trip information to predict optimal content downloads in advance. This preliminary action allows the system to prepare content strategically based on projected needs, improving efficiency before the trip occurs.
Data Source
AI summary
Systems and methods are described herein for retrieving an information file from a user profile about a scheduled trip, where access to content may be restricted and identifying, based on the consumption pattern, a first progression point before the scheduled start time and a second progression point after the scheduled end time of the scheduled trip. Based on the difference between the first and second progression points, the media guidance application causes to be stored the respective media assets on a consumption device. Then before departing for the scheduled trip, the media guidance application confirms the first progression points against the current progression point and causes to adjust the stored media assets based on a difference between the current progression point and the first progression point.