Dynamic Playlist Ordering Using Location and Field of View
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Solution Overview
Problem
User-configured playlists require time and effort to construct and manually update, and existing systems fail to dynamically adapt to user preferences and geospatial location, leading to inefficiencies and increased network bandwidth and storage overhead.
Innovation Solution
A system that automatically generates dynamically ordered playlists based on user preferences and geospatial location using a mobile device, integrating location data, field of view detection, and ranking models to create playlists that adapt to user position and field of view, with features like stream conversion and local storage of content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user manually constructs and updates playlists, then content selection accuracy is improved, but time consumption and operational effort increase
Solution Approach 1:
The system enables automated playlist generation where the system itself selects and orders content based on user preferences and geospatial data, eliminating the need for manual user intervention while maintaining relevant content selection
Solution Approach 2:
The system pre-processes geospatial location data and user preference data to automatically generate playlists in advance, so that when users need content, it is already organized and ready without requiring real-time manual configuration
2Adaptability or versatility
If traditional playlist systems are used, then device complexity is reduced, but adaptability to user preferences and location decreases
Solution Approach 1:
The system integrates multiple functions including geospatial location tracking, field of view detection, content ranking, and automated playlist generation into a single unified system, allowing one system to perform what would otherwise require multiple separate components
Solution Approach 2:
The system introduces an automated ranking module as an intermediary that processes geospatial and user preference data to generate optimized playlists, mediating between raw data inputs and the final playlist output without requiring direct user manipulation
3Measurement precision
If manual playlist updates are performed, then content relevance is maintained, but network bandwidth and storage overhead increase
Solution Approach 1:
The system performs automated playlist generation and content selection in advance based on pre-captured geospatial and user preference data, reducing the need for frequent real-time network updates and minimizing storage overhead by only retrieving necessary content
Solution Approach 2:
The system automatically monitors and updates playlists based on changing user location and preferences without requiring manual user input, maintaining content relevance through autonomous operation while reducing network and storage resource consumption
Data Source
AI summary
A system for dynamic playlist generation may comprise a mobile device including a display a processor a tangible, non-transitory electronic memory in electronic communication with the processor, and a set of computer readable code on the non-transitory electronic memory, including a user interface (UI) module executable to display system information and receive user inputs a mapping module executable to request and receive location data from a location data source, to request and receive map data from a map data source, and to integrate the map data and the location data in real time to generate a dynamic map displayable via the UI module, a Field of View (FOV) detector module executable by the processor to determine a current field of view of the dynamic map as displayed on the display via the UI module, and a list generator module.