Dynamic Deletion Classifier for Recorded Media Assets
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Solution Overview
Problem
Current systems for managing recorded content on devices lack fine-grained deletion recommendations and do not effectively update recommendations based on new information, leading to cumbersome manual deletion processes and coarse granularity suggestions.
Innovation Solution
A media guidance application that classifies recorded content using a deletion classification database, updating deletion classifiers based on recent events and user interactions, allowing for bi-directional movement of content between deletion categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current systems use single-rule deletion recommendations, then the system complexity is low, but the deletion recommendation precision is coarse
Solution Approach 1:
The patent segments the deletion recommendation system into multiple independent rules, each evaluating different aspects of recorded content (e.g., content age, view status, storage capacity, user preferences). These rules work together to provide fine-grained, multi-dimensional classification rather than relying on a single complex rule, thereby improving precision while managing system complexity through modular rule design.
Solution Approach 2:
The system dynamically adjusts deletion recommendations by continuously evaluating recorded content against multiple rules and updating recommendations based on changing conditions (e.g., new content added, content viewed, storage capacity changes). This dynamic multi-rule evaluation allows the system to adapt to new information and provide updated recommendations without requiring complete system redesign.
2Ease of operation
If manual deletion determination is used, then the system complexity is low, but the user effort and time required is high
Solution Approach 1:
The system performs self-service by automatically evaluating recorded content against multiple rules and generating deletion recommendations without requiring user intervention. The system monitors storage capacity, content characteristics, and user behavior patterns autonomously, then presents tailored deletion recommendations that users can review and approve with minimal effort, significantly reducing both user effort and time compared to manual determination.
Solution Approach 2:
The system implements feedback loops where user responses to deletion recommendations (approval, rejection, or modification) are fed back into the rule evaluation process. This feedback mechanism allows the system to learn from user preferences and refine future recommendations, improving ease of operation over time while reducing the time users need to spend on deletion decisions.
3Reliability
If deletion recommendations are not updated based on new information, then the system complexity is low, but the recommendation relevance deteriorates
Solution Approach 1:
The system maintains continuous evaluation of recorded content against multiple rules, constantly monitoring for changes in content status, storage capacity, and user behavior. This continuous action ensures that deletion recommendations remain relevant and up-to-date without requiring periodic manual system updates, achieving high reliability through persistent rule-based assessment while managing complexity through automated continuous monitoring.
Solution Approach 2:
The system implements periodic re-evaluation of recorded content against the rule set at defined intervals or triggered by specific events (e.g., when storage capacity changes, when new content is added, or when users interact with recommendations). This periodic action maintains recommendation relevance by systematically updating assessments based on new information while managing system complexity through structured, scheduled evaluation rather than continuous complex processing.
Data Source
AI summary
Systems and methods are disclosed herein for classifying, based on most recent information associated with recorded content, the recorded content to an appropriate deletion classifier. A media guidance application may receive information about an event that is associated with a recorded media asset. The media guidance application may determine, based on the event and a current deletion classifier associated with the recorded media asset, whether the current deletion classifier associated with the recorded media asset needs to be updated to a new deletion classifier. The media guidance application may make this determination by accessing a deletion classification database containing rules for classifying recorded media assets into a variety of deletion classifiers. Upon determining that the current deletion classifier associated with the recorded media asset needs to be updated to a new deletion classifier, the media guidance application may update the current deletion classifier to the new deletion classifier.