Smart Sharing for Digital Asset Libraries
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Solution Overview
Problem
The challenge of efficiently populating a shared digital asset library with minimal user input and avoiding unintended transfers of digital assets is unresolved, particularly in scenarios where end-users have large collections of photos and videos.
Innovation Solution
Implementing smart sharing options based on sharable DA triggers, context metrics, and user input, including automation and machine learning to select and transfer appropriate digital assets to a shared library during onboarding, near-live, and deferred intervals, while considering privacy and customization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automation is used to populate the shared DA library, then productivity is improved, but reliability deteriorates due to unintended DAs being transferred
Solution Approach 1:
The system provides feedback loops where users can review suggested DAs before transfer, correct false positives, and provide feedback that improves the machine learning model's accuracy over time. The system monitors transfer accuracy and adjusts sharing policies based on user corrections and contextual analysis.
Solution Approach 2:
The system introduces an intermediary review layer between automatic selection and final transfer. Machine learning models generate suggestions, but a review mechanism (automated filtering + user confirmation) acts as an intermediary to verify appropriateness before actual transfer, preventing unintended DAs from being moved.
2Reliability
If manual user selection is used to populate the shared DA library, then reliability is improved, but productivity deteriorates due to tedious manual process
Solution Approach 1:
The system performs preliminary actions by pre-analyzing DAs, pre-generating sharing suggestions, and pre-evaluating contextual metrics before user review. This prepares the data in advance so users only need to review and confirm, rather than manually selecting from scratch, thus speeding up the process while maintaining reliability.
Solution Approach 2:
The system enables self-service by allowing users to configure their own sharing policies, define trusted sources, set privacy preferences, and customize sharing rules. The machine learning model adapts to individual user preferences automatically, reducing the need for continuous manual intervention while maintaining accurate selection.
3Ease of operation
If automation level is increased, then ease of operation is improved, but device complexity increases due to machine learning and contextual analysis
Solution Approach 1:
The system segments the sharing functionality into distinct modular components: trusted source identification, contextual metric analysis, machine learning suggestion generation, user preference management, and policy enforcement. Each module handles a specific aspect of the sharing process, making the overall complex system manageable and maintainable while providing high-level automation to users.
4Measurement precision
If contextual analysis is performed to improve sharing accuracy, then measurement precision is improved, but use of energy increases due to DA analysis and machine learning
Solution Approach 1:
The system performs contextual analysis periodically rather than continuously. Machine learning models evaluate DAs at scheduled intervals or triggered by specific events (new DA capture, user policy changes), rather than constantly analyzing all DAs. This reduces energy consumption while maintaining accurate determination of sharable DAs through periodic re-evaluation.
Data Source
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AI summary
This disclosure relates to systems and methods related to smart sharing options for a shared digital asset (DA) library. An example method performed by a system includes: initiating a camera session; identifying a sharable DA trigger; capturing a DA during the camera session after identifying the sharable DA trigger; and selecting the captured DA for the shared DA library based on the identified sharable DA trigger.