Dynamic Retention Periods for Digital Content via Machine Learning
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Solution Overview
Problem
Portable computing devices face storage capacity issues due to proliferating file attachments, requiring users to manually delete files, which is time-consuming and unpleasant, highlighting a need for improved digital content retention management.
Innovation Solution
A computer program product and method that uses a machine learning module to dynamically determine retention periods for digital content based on access patterns and attributes, automatically deleting files after a determined period to manage storage effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If manual deletion of files is implemented to free storage space, then storage capacity is managed, but user time and experience are negatively impacted
Solution Approach 1:
The system automatically manages file retention by analyzing access patterns and determining which files to delete, eliminating the need for manual user intervention. The machine learning model autonomously decides retention periods based on observed user behavior, allowing the system to self-regulate storage capacity without consuming user time.
Solution Approach 2:
The system continuously monitors user access patterns to digital content and uses this feedback to dynamically adjust retention periods. By observing how users interact with files (access frequency, recency, patterns), the system learns and adapts its deletion decisions, creating a closed-loop feedback mechanism that optimizes storage management over time.
2Ease of operation
If digital content is retained indefinitely, then user access convenience is maintained, but storage capacity is exceeded
Solution Approach 1:
The retention period for digital content is not fixed but dynamically adjusted based on observed user access patterns. The system transitions from static retention policies to dynamic, adaptive retention periods that change as user behavior evolves, allowing optimal balance between accessibility and storage utilization.
Solution Approach 2:
The system changes the retention parameter (time duration) based on observed user behavior patterns. By analyzing access frequency, recency, and patterns, the system adjusts the retention period parameter dynamically, extending retention for frequently accessed content and shortening it for rarely accessed content, thereby optimizing both convenience and storage capacity.
3Device complexity
If fixed retention periods are used for digital content, then storage management is simplified, but user behavior patterns are not optimized
Solution Approach 1:
The machine learning model autonomously analyzes user access patterns and determines optimal retention periods without requiring manual configuration or complex user input. The system serves itself by automatically learning from user behavior and adjusting retention policies, maintaining simplicity while achieving high adaptability to individual user patterns.
Solution Approach 2:
The system implements a feedback loop where user access patterns are continuously monitored and fed back into the machine learning model. This feedback mechanism allows the system to adapt retention periods based on actual user behavior rather than relying on fixed, pre-determined values, achieving both simplicity and adaptability simultaneously.
Data Source
AI summary
Provided are a computer program product, system, and method for dynamic determination of retention periods for digital content. Metadata is generated for instances of digital content including an access pattern of the digital content by a user of the computing device, attributes of the digital content, and a retention period during which the digital content stored is retained in the storage. A machine learning module is trained with input comprising the metadata for instances of the digital content to produce the retention period of the digital content. Input, comprising metadata determined from digital content, received after training the machine learning module, is provided to the machine learning module to produce an output retention period for the digital content received after the training. The output retention period is used to determine when to delete the digital received after the training content from the storage.