NLP-Based Hash Tag Matching for Personalized Contact Media
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Solution Overview
Problem
Current mobile devices lack personalized and interactive contact management systems, requiring significant user effort to play custom content like ringtones and videos during contact events, and such content tends to become stale without regular updates.
Innovation Solution
A system that uses a computer-to-computer interface with natural language processing and metadata matching to associate hash tags with messages, enabling sender-controlled contact media content to be played on recipient devices during contact events, such as calls or texts, using voice and data layer transmission mediums for coordinated playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sender-controlled contact media content is implemented, then user engagement and personalization are improved, but device complexity and content management overhead increase
Solution Approach 1:
A content management server acts as an intermediary between senders and recipient devices. The server handles content storage, delivery, and management, freeing recipient devices from complex content management responsibilities while enabling personalized contact media content to be played on recipient devices during contact events
Solution Approach 2:
The system enables senders to autonomously create, upload, and manage their own contact media content through user interfaces on their devices. The content is then automatically associated with sender identifiers and delivered to recipient devices without requiring recipient intervention for content creation or management
2Measurement precision
If natural language processing and metadata matching are used to associate hash tags with messages, then content matching accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs natural language processing on messages and generates metadata and hash tags in advance, before content matching is needed. This preliminary processing allows for accurate content matching later without significant time delays during actual contact events
Solution Approach 2:
The system replaces manual content selection and matching with automated natural language processing and metadata-based matching algorithms. This substitution enables accurate content association without requiring user intervention, reducing both time and computational overhead
3Duration of action of stationary object
If content is dynamically updated and stored on external servers, then content freshness is improved, but data transmission and storage requirements increase
Solution Approach 1:
The system extracts and stores contact media content on external content management servers rather than storing all content locally on recipient devices. This extraction enables dynamic content updates with minimal data transmission to recipient devices, as only content delivery notifications or updates are transmitted rather than entire content libraries
Data Source
AI summary
A system may facilitate communication between a messaging platform associating a hash tag with a message and a content matching platform. A natural language processing (NLP) facility may produce at least one of an understanding, theme, emotion, and intent of a message. A metadata matching facility may identify candidate hash tags by determining similarity from a pool of hash tags with an output of the NLP, where the content matching facility communicates at least one of the candidate hash tags to the messaging platform. In another aspect, a method may include communicating a message to a content matching platform, and processing text of the message with NLP to generate NLP output including at least one of a theme, understanding, intent, and emotion of the message. A candidate hash tag may then be determined and communicated based on similarity of the NLP output with a plurality of hash tags.