Digital Messaging Analysis for Cross-Platform Relationship Identification
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Solution Overview
Problem
Conventional social networking systems are limited in identifying user relationships beyond their own data constructs, failing to recognize connections established through other services and lacking automated methods to analyze digital messaging content for shared interests.
Innovation Solution
The system analyzes digital messages to create user personas, identifies top contacts based on interaction frequency, and updates a relationship matrix to indicate shared interests, enabling automated recommendations for user groups or activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional social networking systems use existing social network graphs to establish relationships, then relationship establishment is straightforward within the platform, but the system cannot recognize connections from other services or platforms
Solution Approach 1:
The patent applies universality by making the relationship identification system multi-functional across different platforms. Instead of being limited to one social network's data, the system processes digital messages from multiple messaging services (SMS, MMS, email, instant messaging) to identify relationships that exist across different platforms, making the social graph construction universal rather than platform-specific
Solution Approach 2:
The patent uses digital message content as an intermediary to bridge connections between different platforms. By analyzing message content, sender-receiver patterns, and communication frequency across various messaging services, the system mediates the discovery of relationships that would otherwise remain invisible to any single platform's closed social graph
2Productivity
If manual methods are used to identify shared interests and create relationships, then relationship accuracy may be high, but the process is time-consuming and inefficient
Solution Approach 1:
The patent implements self-service by enabling the system to automatically analyze digital messaging content, extract user interests, and identify relationships without human intervention. The automated processing of message metadata, content analysis, and relationship matrix generation allows the system to perform relationship identification tasks independently, dramatically improving productivity while minimizing time loss
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Instead of humans manually reviewing messages to identify relationships, the system uses automated algorithms to process digital messaging content, extract patterns, and generate relationship matrices, substituting mechanical human analysis with efficient computational processing
3Measurement precision
If the system analyzes all digital messages to identify relationships, then relationship identification becomes comprehensive, but network traffic and processing load increase
Solution Approach 1:
The patent applies extraction by selectively removing and analyzing only the essential elements needed for relationship identification from digital messages. Instead of processing entire message contents, the system extracts key features such as sender-receiver pairs, communication frequency, message metadata, and specific interest-related keywords, reducing the data volume requiring analysis while maintaining identification accuracy
Solution Approach 2:
The patent implements partial action by analyzing a representative subset of digital messaging data rather than every single message. The system processes message samples, metadata, and key interaction patterns that are sufficient to identify relationships and shared interests, avoiding the excessive energy consumption that would result from comprehensive analysis of all messages
Data Source
AI summary
Disclosed are systems and methods for improving interactions with and between computers searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The methods and systems analyze digital message content in digital communication systems to automatically identify shared user interest(s), to automatically create computerized relationship matrix data identifying user connections, or relationships, using identified shared user interest(s), and to automatically provide a recommendation using the shared user interest and user relationships formed using the shared user interest.