NLP Event Prioritization for Urgent Security Message Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Security systems face issues with undesired events such as component malfunctions, and traditional customer service systems are inefficient in prioritizing and addressing these events due to chronological queuing of customer requests.
Innovation Solution
Implementing a natural language processing (NLP) system to analyze security system events, determine urgency and priority, and automatically route messages to appropriate agents or trigger actions based on NLP models, enabling real-time response and automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional chronological queuing is used to handle customer service requests, then all requests are processed in order of receipt, but urgent security events are not prioritized and response time is delayed
Solution Approach 1:
The system changes the parameter of request prioritization from chronological order to urgency-based order by using NLP to analyze message content and determine event priority levels. This allows urgent security events to be identified and routed to available agents immediately rather than waiting in chronological queue, directly resolving the contradiction between response priority accuracy and response time.
Solution Approach 2:
The patent replaces the mechanical chronological queuing system with an intelligent NLP-based prioritization system. Instead of simply processing requests in the order they arrive, the system uses natural language processing to analyze message content, determine event urgency, and dynamically prioritize requests, thereby improving both response priority accuracy and reducing response time for critical events.
2Productivity
If NLP analysis is implemented to determine event priority, then urgent events can be prioritized and responded to faster, but system complexity increases
Solution Approach 1:
The patent introduces an NLP-based message analysis component as an intermediary between message receipt and agent routing. This intermediary analyzes the content of customer messages to determine event priority and type, then routes messages to appropriate agents based on the analysis. While this adds a processing layer, it enables automated prioritization that improves event processing efficiency without requiring complete system redesign.
Solution Approach 2:
The system segments the customer service workflow into distinct stages: message receipt, NLP analysis for priority determination, and agent routing. By dividing the process into manageable segments with clear responsibilities, the system can implement sophisticated NLP-based prioritization without overwhelming complexity, as each segment handles a specific function independently.
3Ease of operation
If automated NLP-based routing is used, then response time improves and agents are better matched to events, but implementation cost and technical complexity increase
Solution Approach 1:
The system implements automated self-service routing where the NLP analysis automatically determines event priority and routes messages to appropriate agents without manual intervention. The system serves itself by using AI to analyze message content, classify events, and distribute workloads, thereby improving agent routing efficiency while the automation handles the complexity internally rather than requiring manual configuration for each routing decision.
Data Source
AI summary
A language device is described. A language device includes processing circuitry configured to: determine an urgency level of a message based at least in part on a natural language processing (NLP) model; determine a priority of an event associated with the message based at least in part on the urgency level; and order the message relative to a plurality of other messages in a customer queue based at least in part on the priority of the event associated with the message.