Graph-Based User Connection Data Processing for Communication Apps
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Solution Overview
Problem
Conventional technologies lack the ability to programmatically determine and present user-connection data, such as relationships and optimal paths between users, which limits the functionality of electronic communication and collaboration applications, leading to inefficient manual configuration and lack of contextual information in meetings and communications.
Innovation Solution
The implementation of a graph data structure to represent user relationships, where user-connection data is collected, compared, and assembled into a graph, allowing for the determination of optimal paths and presentation of insights about user connections, using edge weighting and context-based processing to enhance communication applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional technology is used, then manual configuration can be performed, but productivity is reduced due to inefficient manual configuration and lack of automated user-connection data determination
Solution Approach 1:
The system automatically determines user-connection data by comparing user profiles and extracting relationship information without requiring manual configuration. The computing device autonomously processes user data, identifies connections, and presents relevant information, eliminating the need for manual setup while improving configuration efficiency.
2Productivity
If user-connection data is collected and processed programmatically, then productivity is improved through automated determination, but device complexity increases due to the need for graph data structures and comparison algorithms
Solution Approach 1:
The computing device utilizes existing multi-functional capabilities including profile storage, data comparison algorithms, and presentation interfaces that are already part of the system. By leveraging these universal components, the device can determine user-connection data without requiring entirely new dedicated hardware or complex standalone systems.
3Adaptability or versatility
If contextual information about user relationships is provided, then adaptability is improved for communication applications, but loss of information increases due to the need to process and filter large amounts of user connection data
Solution Approach 1:
The system extracts and presents only the most relevant user-connection information based on the current context and user needs. By filtering and selecting specific relationship data rather than processing all available connection information, the system provides adaptable contextual information while minimizing information loss through targeted data extraction.
Data Source
AI summary
Technology is disclosed for controlling the processing and presentation of user-connection data on computing devices to provide improved electronic communications applications and user computing experiences. User-connection data may be programmatically determined or inferred from the user data for a plurality of users. The user-connection data may be assembled into a graph data structure, which may be further processed to determine optimal paths connecting users and to derive information insights. Aspects of information insights may be presented to a user and/or consumed by a computing application or service to provide an improved user computing experience.