Smart File Sharing Suggestion System
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Solution Overview
Problem
Existing systems lack an efficient method to identify and suggest the most appropriate users for sharing electronic files during editing, leading to inefficiencies in file sharing processes.
Innovation Solution
A system that infers a user's intent to share an electronic file and calculates probabilities for each potential user based on various factors, including file management events, content analysis, metadata, and historical interactions, to suggest the most suitable users for sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual user selection for file sharing is used, then user control is maintained, but time consumption and operational complexity increase
Solution Approach 1:
The system performs automatic user suggestion and probability calculation without requiring manual user input for candidate selection. The computer autonomously analyzes file content, metadata, and user profiles to generate ranked sharing suggestions, allowing the system to serve itself in the user selection process.
Solution Approach 2:
The system pre-calculates and stores user profiles, file metadata, and historical sharing patterns before the actual file sharing operation. When a user initiates sharing, the system quickly retrieves pre-processed data and generates suggestions instantaneously, rather than performing heavy analysis during the sharing moment.
2Measurement precision
If comprehensive user analysis is performed to improve suggestion accuracy, then sharing precision increases, but processing consumption increases
Solution Approach 1:
The system divides the user analysis into separate modular components: file content analysis, metadata extraction, user profile matching, and historical behavior analysis. Each module processes specific aspects independently and returns results to the main system, allowing for efficient resource management and selective execution based on needs.
Solution Approach 2:
The system dynamically adjusts analysis depth and computational parameters based on file type, user preferences, and system load conditions. For routine files, simplified matching is used; for complex or sensitive files, more comprehensive analysis is performed, optimizing the balance between accuracy and resource consumption.
3Measurement precision
If probability calculation for multiple users is performed, then suggestion relevance improves, but network bandwidth consumption increases
Solution Approach 1:
The system extracts and processes only the most relevant user attributes and file characteristics needed for matching, rather than analyzing complete user profiles and file contents. This selective extraction reduces data transmission requirements while maintaining suggestion quality.
Solution Approach 2:
The system calculates probability scores for a limited number of top candidate users rather than all possible users in the organization. By focusing computational resources on the most likely candidates based on initial filtering criteria, the system achieves high relevance with reduced processing and network overhead.
Data Source
AI summary
Described herein is a system and method for suggesting other user(s) with whom to share an electronic file. During editing of the electronic file, an intent of a user to share the electronic file can be inferred. For each of a plurality of other users, a probability indicative of a likelihood that the user would like to share the electronic file with the particular other user is calculated. Based upon the calculated probabilities, at least one other user to suggest to the user for sharing of the electronic file is identified. Information regarding the identified at least one other user is displayed. Based upon a user selection of another user to share the electronic file with, an algorithm used to calculate the probabilities can be updated.