Neural Network Message Routing for Dynamic Distribution
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Solution Overview
Problem
Existing distribution list systems are inefficient in filtering out inappropriate or unsolicited messages, leading to resource waste and nuisance, as static rules fail to adapt to dynamic user interactions and message contexts.
Innovation Solution
A machine learning-based system using natural language processing and multi-layered neural networks dynamically evaluates message contexts and user interactions to determine appropriate recipient lists, adjusting rules based on user roles, message sensitivity, and relevance, thereby refining message routing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static rules are used to control distribution lists, then system simplicity is maintained, but adaptability to dynamic scenarios deteriorates
Solution Approach 1:
The patent implements dynamic rules that automatically adjust based on real-time analysis of message content, user behavior patterns, and contextual factors. The system transitions from static predefined rules to dynamic adaptive rules that learn and evolve, allowing the distribution list control mechanism to adapt to changing scenarios while maintaining manageable complexity through automated decision-making algorithms.
Solution Approach 2:
The system employs self-service mechanisms where the automated system independently analyzes messages, evaluates user preferences, and makes distribution decisions without requiring manual configuration or intervention. This self-service capability enables the system to adapt to dynamic scenarios autonomously, reducing the burden on users while improving adaptability.
2Productivity
If all users on distribution list receive every message, then complete information dissemination is achieved, but resource waste increases
Solution Approach 1:
The patent extracts and removes unnecessary message transmissions by analyzing each message's relevance to individual users before distribution. The system identifies and excludes users for whom a message is not relevant, inappropriate, or unsolicited, thereby eliminating wasted resource consumption on unnecessary transmissions while ensuring that relevant messages reach all intended recipients.
Solution Approach 2:
Instead of applying uniform message distribution to all users, the system implements partial action by selectively distributing messages only to relevant subsets of users based on real-time analysis. This partial distribution approach optimizes resource utilization by avoiding excessive transmissions to users who do not need the information, thereby improving overall message delivery efficiency.
3Adaptability or versatility
If predefined rules limit message distribution, then inappropriate messages are reduced, but flexibility in common scenarios deteriorates
Solution Approach 1:
The patent changes the parameters of rule-based systems from fixed static parameters to dynamic adjustable parameters. The system continuously evaluates multiple factors including message content characteristics, user behavior patterns, temporal context, and relevance metrics, adjusting distribution decisions based on changing parameters rather than rigid predefined rules. This enables flexibility across diverse scenarios while managing complexity through systematic parameter evaluation.
Solution Approach 2:
The system introduces an intermediary automated analysis layer between the sender and distribution list that mediates message routing decisions. This intermediary evaluates messages against multiple criteria and user preferences, providing flexible adaptation to various scenarios without requiring complex manual rule configurations. The intermediary acts as an intelligent broker that simplifies the overall system architecture while enabling nuanced decision-making.
Data Source
AI summary
Techniques for communication evaluation and routing are provided. A communication for distribution to a plurality of users is received from a sending user, and the communication is parsed using one or more natural language processing (NLP) techniques to determine a context. A plurality of scores is generated for the plurality of users by processing the context using a machine learning (ML) model. It is determined, based on a first score of the plurality of scores corresponding to a first user of the plurality of users, to transmit the communication to the first user. It is further determined, based on a second score of the plurality of scores corresponding to a second user of the plurality of users, to refrain from transmitting the communication to the second user. Additionally, the system transmits the communication to the first user, and refrains from transmitting the communication to the second user.