Media Sharing Recipient Suggestions via Contextual Relevance
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Solution Overview
Problem
Users face a laborious and time-consuming process when selecting recipients to share video and audio media items due to numerous communication options and extensive contact lists across different platforms.
Innovation Solution
An apparatus comprising a classification module to categorize media items, a correlation module to determine context information, and an estimation module to calculate relevance values for contacts based on sharing history, generating suggested recipients for efficient media sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually select recipients from extensive contact lists across multiple communication platforms, then sharing options remain comprehensive and flexible, but the process becomes laborious and time-consuming
Solution Approach 1:
The system automatically identifies and suggests sharing recipients by analyzing the user's communication history and contact information across multiple platforms, eliminating the need for manual selection. The estimation module computes relevance values and generates suggested recipient lists autonomously based on sharing patterns and contextual information.
Solution Approach 2:
The system pre-processes communication history and contact data to create organized contact lists and relevance value calculations before the sharing action occurs. By preparing suggested recipient lists in advance based on historical patterns, the system reduces the time required at the moment of sharing.
2Adaptability or versatility
If the system provides comprehensive contact lists across multiple communication platforms, then sharing options remain versatile, but the complexity of recipient selection increases
Solution Approach 1:
The system segments the comprehensive contact list into priority-based groups (e.g., high, medium, low relevance) and presents them in a hierarchical manner. This segmentation allows users to see only the most relevant contacts first while maintaining access to the full list if needed, reducing interface complexity.
Solution Approach 2:
The system introduces an intermediary layer (the estimation module and suggested recipient list) between the raw contact data and the user interface. This intermediary processes and filters the comprehensive contact information, presenting a simplified view to users while preserving the underlying versatility.
3Measurement precision
If the system analyzes extensive communication history to generate accurate recipient suggestions, then suggestion accuracy improves, but processing requirements and system complexity increase
Solution Approach 1:
The system uses the user's actual sharing decisions as feedback to refine and update the estimation module's models. By continuously learning from user behavior patterns and adjusting relevance value calculations accordingly, the system improves accuracy over time without requiring increasingly complex analysis algorithms.
Data Source
AI summary
Improved techniques for media item sharing are described. In one embodiment, for example, an apparatus may comprise a classification module to assign a media item to a content category, a correlation module to determine context information for the media item, and an estimation module to determine a set of relevance values for a set of contacts based at least in part on a sharing history and to generate a set of suggested recipients for the media item based at least in part on the set of relevance values and the set of contacts. Other embodiments are described and claimed.