Content-Based Message Routing for Multi-Issue Recipient Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing messaging systems often fail to efficiently route messages with multiple issues to the appropriate recipients within organizations, leading to delays and inefficiencies as staff re-routes messages manually, and recipients may not be able to act on the issues.
Innovation Solution
A system that analyzes messages to identify components based on content, applies machine learning to select appropriate recipients for each component, and transmits them individually, using natural language processing to partition and route relevant parts of the message to the correct individuals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual message routing is used, then staff can route messages to appropriate individuals, but delays occur and efficiency decreases
Solution Approach 1:
The system automatically analyzes message content, identifies components, determines appropriate recipients, and routes messages without human intervention. The routing function serves itself by using automated content analysis and machine learning models to select recipients, eliminating the need for manual staff routing and thereby reducing delays while improving efficiency.
2Adaptability or versatility
If messages are routed to a single recipient, then the message structure is simple, but the recipient may not be able to act on all issues
Solution Approach 1:
The system segments a message into multiple components based on content analysis, identifying distinct issues or topics within the message. Each component is then routed to the most appropriate recipient based on their capabilities and expertise. This segmentation allows the system to handle complex multi-issue messages by distributing them to multiple specialized recipients, improving adaptability while managing complexity through automated component identification.
Solution Approach 2:
The system applies different routing criteria and content analysis to different components of the same message based on their specific nature. Each message component is evaluated individually and routed to the recipient best suited for that particular issue, rather than applying a uniform routing rule to the entire message. This local quality approach ensures optimal recipient matching for each component while maintaining system manageability.
3Productivity
If automated routing is implemented, then efficiency improves, but the system complexity increases
Solution Approach 1:
The system employs a universal content analysis engine that handles multiple message types, formats, and complexities through a single automated routing platform. The machine learning models and content analysis components serve multiple functions: identifying message components, determining recipient suitability, and managing routing decisions. This multi-functionality reduces overall system complexity by consolidating diverse routing tasks into unified automated processes rather than requiring separate mechanisms for each message type.
Data Source
AI summary
Techniques for routing a message based on the content of the message include detecting a message and computing a plurality of message components comprised in the message. The system processes the respective message components to determine respective sets of message attributes for the respective message components. The system applies a machine learning model to a set of message attributes to select a recipient for the associated message component and transmits the message component to the selected recipient.