Media Asset Tagging via Verbal Input and Playback Adjustment
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Solution Overview
Problem
Traditional systems for associating attributes or tags with media assets are burdensome for users, as they often rely on manual selection or crowd sourcing, lacking efficient automation.
Innovation Solution
The system automatically associates tags or attributes with media assets based on verbal input and playback adjustments by processing verbal input into digital form, cross-referencing it with databases to identify expected playback adjustments, and updating attributes accordingly, with a focus on user feedback and timing thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual user selection or crowd sourcing is used to associate attributes with media assets, then attribute accuracy can be maintained, but user burden increases significantly
Solution Approach 1:
The system automatically performs the tagging function by monitoring user playback adjustments and verbal expressions without requiring users to manually select or input tags. The media asset management system serves itself by inferring attributes from observed user behavior patterns, eliminating the need for users to actively participate in the tagging process while maintaining accurate attribute association
Solution Approach 2:
The system uses user playback adjustments (fast-forward, rewind, pause) and verbal expressions as feedback signals to automatically determine appropriate tags. By continuously monitoring user interactions with the media asset and processing this feedback through natural language processing and pattern recognition, the system automatically associates relevant attributes without user intervention
2Extent of automation
If automated tagging is implemented without user feedback, then user burden is reduced, but attribute accuracy deteriorates
Solution Approach 1:
The system incorporates real-time user feedback through playback adjustments and verbal expressions to guide the automated tagging process. User actions such as fast-forwarding through boring segments or pausing during interesting moments serve as explicit feedback signals that help the system accurately infer user preferences and associate appropriate attributes with media assets
Solution Approach 2:
The system replaces manual mechanical tagging operations with automated electronic processing of user behavior data. By using natural language processing, pattern recognition, and machine learning algorithms to analyze user playback adjustments and verbal expressions, the system achieves accurate automated tagging without requiring manual user input
3Speed
If verbal input processing and database cross-referencing are performed in real-time, then tagging responsiveness is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary processing of verbal input by converting speech to text and pre-processing the digital representation before cross-referencing with the database. This preliminary action prepares the data in advance, enabling faster and more efficient matching with stored attributes during real-time operation, thereby improving responsiveness while managing processing complexity
Data Source
AI summary
Systems and methods for automatically tagging a media asset are provided. Verbal input is received from a user while the user is accessing the media asset. A request to adjust playback of the media asset is received from the user. Responsive to receiving the verbal input and the request, a combination of the verbal input and the request is cross-referenced with an attribute database to identify an attribute associated with the combination. The identified attribute is associated with the media asset.