Connected Contact Identification via Communication Graph Analysis
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Solution Overview
Problem
Current database systems lack the ability to effectively identify connected contacts within and outside organizations due to the unavailability of structured communication data and robust connection graphs, making it difficult to identify influential individuals for communication and sales purposes.
Innovation Solution
A connected contact identification service that processes communication messages using natural language processing to generate a graph representing relationships, calculates connection metrics, and identifies well-connected targets by analyzing communication data between users and external organizations, even without direct access to external communication data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current database systems are used without structured communication data, then system simplicity is maintained, but the ability to identify connected contacts and influential individuals is lost
Solution Approach 1:
The system pre-processes and structures communication data from multiple sources (emails, calendar events, service tickets, text messages, voice calls, social media messages) into a standardized format before analysis. This preliminary structuring enables subsequent identification of connection graphs and influential contacts without adding complexity during the actual identification process
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw communication data into structured connection graphs. This intermediary layer includes components that extract entities, relationships, and interaction patterns from unstructured data, then presents processed information to the identification algorithms, isolating the complexity from the core identification function
2Adaptability or versatility
If communication data from external organizations is not collected, then data privacy and security are maintained, but the ability to identify influential contacts outside the organization is reduced
Solution Approach 1:
The system implements a universal communication data collection framework that handles multiple data types (emails, calendar events, service tickets, text messages, voice calls, social media messages) from both internal and external sources through a single integrated process. This multi-functional approach enables identification of connected contacts across organizational boundaries while maintaining consistent data processing standards
Solution Approach 2:
The system creates structured copies of communication data from external organizations without requiring direct access to their internal systems. By capturing and structuring communication instances that involve external contacts (emails exchanged, calendar events, service tickets), the system builds connection graphs that reflect external relationships while respecting organizational data boundaries
3Measurement precision
If connection graphs are not robustly constructed, then processing speed is maintained, but the accuracy of identifying influential contacts is reduced
Solution Approach 1:
The construction of robust connection graphs is segmented into distinct processing stages: data collection from multiple sources, entity extraction and normalization, relationship identification, interaction pattern analysis, and graph construction. Each segment processes specific aspects of the data independently, allowing for optimized processing at each stage while building toward the complete connection graph
Solution Approach 2:
The system performs preliminary processing of communication data including entity extraction, normalization, and relationship identification before constructing the final connection graph. This preliminary action prepares the data in a structured format that accelerates the graph construction process while ensuring accuracy through pre-validated relationship patterns
Data Source
AI summary
A database server may analyze interaction data including communication to generate a graph representation of various users and connections between the users. The database server may utilize the graph representation of connections to identify sufficiently connected target user identifiers in one or more external organizations. A connection metric may be assigned to each user identifier of one or more groups of user identifiers generated using the graph representation, and the target user identifiers may be identified based on the connection metrics.