Intelligent File Name Suggestions for Digital Content
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Solution Overview
Problem
Conventional naming systems for digital content items often generate non-intuitive and generic names, making it difficult for users to find specific files, as they rely on assumptions about content types and ignore individual naming preferences.
Innovation Solution
A machine-learning based system that trains on previously named content items by a user or group to suggest names based on learned naming patterns, providing intuitive and descriptive suggestions within a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional naming systems use automatically generated generic names (e.g., date and timestamp), then naming speed is improved, but file identifiability deteriorates
Solution Approach 1:
The system automatically analyzes the content of each file and generates descriptive names without requiring manual user input. The naming system serves itself by extracting meaningful information from file contents, metadata, and contextual data to create identifiable names while maintaining high naming speed.
Solution Approach 2:
The system dynamically adjusts naming parameters based on file type, content characteristics, and user preferences. Instead of using fixed generic formats, the system changes naming parameters (such as including file content descriptors, hierarchical paths, or contextual tags) to optimize both naming efficiency and file identifiability for different scenarios.
2Loss of information
If conventional naming systems analyze text document content to suggest names, then name descriptiveness is improved, but system versatility deteriorates
Solution Approach 1:
The system is designed to handle multiple file types (text documents, images, audio, video, spreadsheets, presentations) using a unified naming approach. It extracts relevant information from different file formats and applies consistent naming logic across all types, making the system both descriptive and versatile without being limited to specific content types.
3Device complexity
If conventional naming systems use fixed naming conventions, then system complexity is reduced, but user preference adaptability deteriorates
Solution Approach 1:
The system dynamically adapts naming conventions based on individual user preferences, organizational standards, and contextual requirements. Instead of using fixed rigid rules, the system adjusts naming parameters, descriptors, and formats in real-time to match user expectations and organizational needs while maintaining underlying consistent logic.
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions, corrections, and preferences are learned and used to refine future naming suggestions. The system monitors user behavior patterns and adjusts its naming conventions accordingly, creating a adaptive loop that improves personalization without significantly increasing system complexity.
Data Source
AI summary
One or more embodiments of a content naming system provide machine-learned name suggestions to a user for naming content items. Specifically, an online content management system can train a machine-learning model to identify a naming pattern from previously stored content items corresponding to a user account of the user. The online content management system uses the machine-learning model to determine a plurality of name suggestions for naming a content item associated with the user account. One or more embodiments provide graphical elements corresponding to the name suggestions within a graphical user interface. The user can select one or more graphical elements to add the corresponding name suggestion(s) to the name of the content item.