Dynamic Playlist Generation via Mood Detection and Tag Allocation
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Solution Overview
Problem
Conventional media systems create static playlists that are not updated when new multimedia files are added, leading to obsolescence and requiring manual intervention for users to refresh their playlists.
Innovation Solution
A media system with a mood detection module, tag allocation module, and playlist generation module dynamically generates playlists by analyzing user data from social media, personal databases, and health parameters to assign emotion tags to multimedia files, updating the playlist based on the user's current mood.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually create playlists by selecting individual multimedia files, then the playlist reflects user preferences, but the playlist becomes obsolete when new multimedia files are added to the database
Solution Approach 1:
The patent transforms static playlists into dynamic playlists that automatically adapt to user mood changes and new multimedia files. The system continuously monitors user data from multiple sources (social media, health sensors, browsing history) and automatically regenerates playlists without manual intervention, resolving the contradiction between maintaining relevance and avoiding manual update time investment
Solution Approach 2:
The system performs self-updating by automatically detecting user mood through mined records and regenerating playlists based on current mood state. This eliminates the need for users to manually refresh playlists when new files are added or when their preferences change, as the system serves itself by continuously adapting to user needs
2Ease of operation
If users specify conditions to create playlists automatically, then playlist creation is automated, but the playlist remains static and does not update with new multimedia files
Solution Approach 1:
The patent enhances automated playlist creation by adding dynamic updating capabilities. The system not only creates playlists automatically based on user-specified conditions but also continuously monitors user mood through mined records and automatically regenerates playlists when mood changes or new relevant files are added, making the playlist adaptive rather than static
Solution Approach 2:
The system implements feedback loops by continuously mining user records from social media, health sensors, and browsing history to detect mood changes. This feedback mechanism triggers automatic playlist regeneration, ensuring the playlist remains aligned with current user preferences and newly added multimedia files without requiring manual re-specification of conditions
3Adaptability or versatility
If the system mines records from multiple sources to detect user mood, then playlist personalization is improved, but system complexity increases
Solution Approach 1:
The patent employs a multi-functional mood detection module that mines records from diverse sources (social media platforms, health sensor data, browsing history, purchase behavior) through a unified approach. This universal module handles multiple data types and sources using consistent algorithms for emotion tag allocation and mood determination, achieving high personalization while managing complexity through functional integration rather than separate systems for each data source
Data Source
AI summary
Disclosed is a method and system for dynamically generating a playlist of multimedia files for a user based upon a mood of the user. The system comprises a mood detection module, a tag allocation module, and a playlist generation module. The mood detection module is configured to determine the mood of the user based upon the one or more emotion tags allocated to records of the user. The tag allocation module is configured to categorize the at least one multimedia file under one or more emotion tags based upon the weights assigned to the one or more emotion tags. The playlist generation module is configured to dynamically generate the playlist of multimedia files based upon the mood of the user and the categorization of the at least one multimedia file under one or more emotion tags.