AI Endpoint Selection for Dynamic Text Message Routing
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Solution Overview
Problem
Traditional bot scripts struggle with efficiently processing dynamic and complex data across multiple environments, leading to inefficiencies and increased load on processing resources due to incorrect message routing.
Innovation Solution
Implementing artificial intelligence techniques and machine learning models to enhance bot accuracy in routing communications, allowing for autonomous and intelligent switching between bots and terminal devices during communication sessions, and dynamically selecting endpoints based on user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional bot scripts are used to process data, then the system structure is simple and easy to implement, but the processing efficiency is low and routing accuracy is poor
Solution Approach 1:
The patent replaces traditional mechanical bot scripts with an AI-based routing system that uses machine learning models to analyze user intent and select appropriate communication endpoints. This substitution enables the system to handle complex, dynamic data routing tasks that were impossible for traditional scripts, significantly improving processing efficiency while managing complexity through automated intelligence rather than manual programming.
Solution Approach 2:
The system dynamically changes routing parameters based on real-time analysis of user intent, message content, and endpoint capabilities. By continuously adjusting routing decisions based on varying parameters such as user profile, message context, and endpoint availability, the system achieves high processing efficiency without requiring complex static configurations, thus resolving the contradiction between productivity and device complexity.
2Extent of automation
If bot scripts are configured to detect target outcomes, then task management can be automated, but the configuration becomes challenging and error-prone
Solution Approach 1:
The AI routing system performs self-service by automatically analyzing user intent and selecting appropriate endpoints without requiring manual configuration of detection rules. The machine learning model processes message content, user profiles, and contextual information to autonomously determine routing decisions, eliminating the need for complex script configurations while maintaining high automation levels in task management.
Solution Approach 2:
The system incorporates feedback mechanisms where routing decisions are continuously refined based on interaction outcomes and user responses. This feedback loop enables the AI model to improve its routing accuracy over time, making the system easier to configure initially while maintaining high automation. The feedback mechanism allows the system to learn from past performance and adjust routing strategies without manual reconfiguration.
3Adaptability or versatility
If bot scripts process tasks across multiple environments, then versatility is improved, but the capability remains insufficient for complex dynamic data
Solution Approach 1:
The patent implements a universal AI routing system that can handle diverse communication channels and data formats across multiple environments. The machine learning model is designed to process various types of inputs (text, voice, structured data) and route them to appropriate endpoints regardless of the communication channel or data structure, achieving both high versatility and reliable routing accuracy through unified intelligent processing.
Solution Approach 2:
The system adapts to different environments by dynamically changing routing parameters based on the specific communication channel, data format, and endpoint requirements. The AI model analyzes contextual parameters such as message type, user preferences, and endpoint capabilities to adjust routing decisions in real-time, maintaining high reliability across diverse environments without requiring environment-specific configurations.
Data Source
AI summary
The present disclosure relates generally to providing a concierge service to handle a wide variety of topics and user intents via a text messaging interface. The concierge service can be part of a connection management system that can dynamically manage and facilitate natural language conversations between a user making a request or providing an instruction and one or more endpoints for the purposes of fulfilling the request or instruction.