Multimedia Content Differentiation in Messaging Sessions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in navigating and finding relevant multimedia content within messaging applications due to the high volume of messages and lack of effective methods for differentiating important multimedia documents, leading to inefficiencies in communication sessions.
Innovation Solution
A method that monitors communication sessions, identifies share actions involving media documents, analyzes reaction data to determine relevant media documents, and modifies the session depiction to highlight these documents automatically, using AI and machine learning techniques to surface important content without user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually navigate through messages to find multimedia content, then they can locate specific documents, but the time required to search increases significantly in high-volume communication sessions
Solution Approach 1:
The patent replaces manual mechanical navigation through messages with an automated AI-based system that analyzes reaction data (likes, comments, shares) to automatically identify and surface relevant multimedia documents. This substitution eliminates the need for users to manually scroll and search through high-volume communication sessions, directly reducing search time while maintaining or improving content discovery accuracy.
Solution Approach 2:
The system enables self-service by automatically analyzing reaction data and surfacing relevant content without requiring user intervention or manual searching. The AI system autonomously processes communication session data, identifies important multimedia documents based on engagement metrics, and presents them to users, allowing the system to serve itself rather than relying on manual user navigation.
2Adaptability or versatility
If all multimedia documents are displayed equally in communication sessions, then users can see all shared content, but important documents become indistinguishable from less relevant ones
Solution Approach 1:
The patent applies local quality by differentiating the display or presentation of multimedia documents based on their relevance, as determined by reaction data analysis. Instead of treating all documents uniformly, the system applies different qualities or priorities to different documents, highlighting those with higher engagement (more likes, comments, or shares) while deprioritizing less relevant content. This resolves the contradiction by making important documents distinguishable without completely hiding less relevant ones.
Solution Approach 2:
The system uses visual differentiation (analogous to color changes) to distinguish relevant multimedia documents from less relevant ones. By applying different visual indicators, highlights, or prioritization based on reaction data analysis, the system makes important content stand out in the communication session interface, allowing users to quickly identify which documents warrant their attention without losing access to the full content set.
3Measurement precision
If the system analyzes reaction data to identify relevant media documents, then content relevance improves, but the system complexity increases due to additional processing requirements
Solution Approach 1:
The patent leverages the existing multi-functionality of reaction data (which already serves purposes like user engagement tracking and communication analytics) and repurposes it for relevance determination. By using the same reaction data infrastructure for multiple purposes including content prioritization, the system avoids building separate complex analysis systems, thereby improving relevance accuracy while minimizing additional system complexity.
4Ease of operation
If automated systems surface important content without user input, then user effort decreases, but the automation extent and processing requirements increase
Solution Approach 1:
The system implements self-service by automatically analyzing reaction data and surfacing relevant multimedia documents without requiring any user input or manual searching. The AI system autonomously processes communication session data, identifies important content based on engagement metrics, and presents it to users, completely eliminating the need for user effort in content discovery while managing automation through efficient use of existing reaction data infrastructure.
Data Source
AI summary
A communication session between two or more computing devices is monitored. One or more share actions, that include one or more media documents, are identified in the communication session responsive to the media documents and based on the monitoring the communication session. Reaction data, that is related to the share actions, in the communication session is analyzed in response the share actions and based on the media documents. One or more relevant media documents are determined based on the analyzing the reaction data. The relevant media documents are analyzed based on the media documents. A first depiction rendered by a first of the devices of the communication session is modified based on the relevant media documents.