Virtual Graph for Multi-Channel Communication Analytics
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Solution Overview
Problem
Conventional telecommunications systems, such as SIP and PBX, primarily focus on voice delivery and network efficiency, failing to comprehensively manage the rich digital content and multi-channel communications, which are often represented simplistically as linear analog signals, missing the complexities of omni-channel interactions.
Innovation Solution
A virtual graph system that captures and represents multi-channel communications by organizing data into object representations of nodes, links, and variables, using analytics and machine learning to derive characteristics and provide actionable insights, enabling flexible deployment and enrichment of metadata across various NLU/NLP vendors and platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional telecommunications systems (SIP, PBX) represent multi-channel communications as linear analog signals, then network efficiency is maintained, but the rich dimensions and complexities of omni-channel interactions are lost
Solution Approach 1:
The patent transforms the traditional linear representation of communications into a multi-dimensional graph structure where nodes represent communication entities and edges represent interactions across multiple channels. This dimensional transformation allows the system to capture the rich complexity of omni-channel interactions while maintaining manageable system architecture through graph theory fundamentals.
2Loss of information
If a virtual graph system captures comprehensive multi-channel communication data, then understanding of interactions is improved, but system complexity increases
Solution Approach 1:
The virtual graph system segments communication data into discrete nodes (communication entities) and edges (interactions), allowing comprehensive capture of multi-channel interactions while managing complexity through modular graph elements. Each node and edge can be independently analyzed and processed.
Solution Approach 2:
The graph structure serves multiple functions simultaneously: it represents communication topology, tracks interaction flows, enables analytics processing, and supports machine learning training. This multi-functionality reduces the need for separate systems for each analytical task.
3Adaptability or versatility
If conventional systems focus on voice delivery, then network efficiency is maintained, but management of multi-channel communications is insufficient
Solution Approach 1:
The virtual graph system dynamically adapts to different communication channels and interaction patterns, allowing the network to efficiently handle diverse multi-channel communications. The graph structure automatically updates to reflect changing communication flows and relationships across voice, data, and other channels.
Data Source
AI summary
A technique is directed to methods and systems for generating a virtual graph of multi-channel communications. In some implementations, a virtual graph system provides a virtual graph relationship that manages the representation and captures the information associated with multi-channel communications, such as communications of a user with a corporate contact center. The information can include session and “customer journey” characteristics of the interaction flow beyond the details of the telecommunication infrastructure. Additionally, the virtual graph system can provide a visualization that communicates both historical details and actionable information found in the interaction graph(s).