Automatic Proximity-Based Metadata Tagging for Digital Content
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing digital data management systems face challenges in efficiently locating and organizing vast amounts of digital content due to limited metadata capabilities, which makes manual tagging of data time-consuming and inefficient.
Innovation Solution
A system for automatic data tagging that utilizes information from nearby devices or individuals, acquired through peer-to-peer protocols or central services, to generate metadata for improved search, filtering, and sorting capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging of data is performed to improve metadata quality and search accuracy, then the precision of data location is improved, but the time and effort required increases significantly
Solution Approach 1:
The system enables automatic tagging by having devices self-tag their own data using information from nearby devices. The tagging process occurs autonomously without human intervention, with devices automatically generating and attaching metadata to their data files based on proximity information and device identifiers.
Solution Approach 2:
The patent introduces an intermediary tagging mechanism where nearby devices act as mediators to provide information for tagging. Instead of manual tagging, the system uses intermediate devices in the vicinity to contribute data about location, device type, and user information that automatically becomes metadata for the tagged files.
2Adaptability or versatility
If conventional metadata (date, time, file type) is used to organize data, then the system complexity remains low, but the ability to differentiate and locate specific content is limited
Solution Approach 1:
The system creates a universal tagging framework that works across multiple device types (mobile phones, digital cameras, media players) and data types (photos, videos, audio files). The same proximity-based tagging mechanism serves all devices and file types, providing multi-functional capability without requiring device-specific tagging solutions.
Solution Approach 2:
The patent adds a new dimension to metadata by incorporating spatial proximity information and device context data alongside traditional temporal and file-type metadata. This creates a multi-dimensional tagging system that enables more sophisticated data differentiation and search capabilities beyond conventional single-dimension metadata.
Data Source
AI summary
Data is automatically tagged utilizing information associated with nearby individuals, among other things. Location-based technology is leveraged to enable identification of individuals and associated devices within a distance of a data capture device. User information is acquired from proximate devices directly or indirectly before, during or after data recording. This information can be utilized to tag captured environmental data (e.g., images, audio, video . . . ), amongst other types, to facilitate subsequent location, filtration and/or organization.